Repository: GeoFLAC/DynEarthSol
Pull request #11: Implementation of rate-and-state friction model
Author: login: sungho91
Labels: name: enhancement
description: New feature or request
Created: 2025-03-05T14:25:49Z | Merged: 2025-04-16T08:40:25Z

1. New rheology for rate-and-state friction model; steady-state case (m_d = m_s + (a-b)*log(V/V_0).
2. Adding new adaptive time stepping and mass scaling
3. modification in pseudo-transient loop (disabling suface diffusion during loop)
4. To catch stick-slip events, Adding a condition for printing out when maximum velocity > 1e-6.


Diff:
diff --git a/2vtk.py b/2vtk.py
index 30253c0d..35270358 100755
--- a/2vtk.py
+++ b/2vtk.py
@@ -46,7 +46,7 @@
 output_melting = False
 
 # Calculate heat flow?
-output_heatflux = True
+output_heatflux = False
 conductivity = 3.3
 
 ########################
diff --git a/bc.cxx b/bc.cxx
index f239cdde..40e1664e 100644
--- a/bc.cxx
+++ b/bc.cxx
@@ -619,12 +619,19 @@ void apply_vbcs(const Param &param, const Variables &var, array_t &vel)
 #endif
                 break;
             case 3:
-                v[0] = 0;
+                v[0] = 0.0;
 #ifdef THREED
                 v[1] = 0;
 #endif
                 v[NDIMS-1] = bc_vz1;
                 break;
+            case 4:
+                v[0] = bc_vz1;
+#ifdef THREED
+                v[1] = 0;
+#endif
+                v[NDIMS-1] = 0;
+                break;
             }
         }
     }
@@ -762,8 +769,9 @@ void apply_vbcs_PT(const Param &param, const Variables &var, array_t &vel)
 #else
         const double *x = (*var.coord)[i];
         double ratio, rr, dvr;
-        double vbc_exact_x0 = vbc_applied_x0 * interp1(vbc_vertical_divisions_x0, vbc_vertical_ratios_x0,-x[1]);
-        double vbc_exact_x1 = vbc_applied_x1 * interp1(vbc_vertical_divisions_x1, vbc_vertical_ratios_x1,-x[1]);
+        double vbc_exact_x0 = vbc_applied_x0 * interp1(vbc_vertical_divisions_x0, vbc_vertical_ratios_x0,-x[1]) * 0.0;
+        double vbc_exact_x1 = vbc_applied_x1 * interp1(vbc_vertical_divisions_x1, vbc_vertical_ratios_x1,-x[1]) * 0.0;
+
 #endif
         //
         // X
diff --git a/brc-interpolation.cxx b/brc-interpolation.cxx
index c9505bc9..d00ed913 100644
--- a/brc-interpolation.cxx
+++ b/brc-interpolation.cxx
@@ -233,6 +233,11 @@ void barycentric_node_interpolation(Variables &var,
     interpolate_field(brc, el, old_connectivity, *var.ppressure, *a);
     delete var.ppressure;
     var.ppressure = a;
+  
+    a = new double_vec(var.nnode);
+    interpolate_field(brc, el, old_connectivity, *var.dppressure, *a);
+    delete var.dppressure;
+    var.dppressure = a;
 
     a = new double_vec(var.nnode);
     interpolate_field(brc, el, old_connectivity, *var.dppressure, *a);
diff --git a/dynearthsol.cxx b/dynearthsol.cxx
index 08f80b49..193598ee 100644
--- a/dynearthsol.cxx
+++ b/dynearthsol.cxx
@@ -144,11 +144,15 @@ void init(const Param& param, Variables& var)
 
     compute_volume(*var.coord, *var.connectivity, *var.volume);
     *var.volume_old = *var.volume;
-    compute_mass(param, var, var.max_vbc_val, *var.volume_n, *var.mass, *var.tmass, *var.hmass, *var.tmp_result);
+    apply_vbcs(param, var, *var.vel); // Due to ATS, this should be called before compute_mass  
+    var.dt = compute_dt(param, var);  // Due to ATS, this should be called before compute_mass
+    compute_mass(param, var, var.max_vbc_val, *var.volume_n, *var.mass, *var.tmass, *var.hmass, *var.ymass, *var.tmp_result);
+
     compute_shape_fn(var, *var.shpdx, *var.shpdy, *var.shpdz);
 
     create_boundary_normals(var, *var.bnormals, var.edge_vectors);
-    apply_vbcs(param, var, *var.vel);
+    // apply_vbcs(param, var, *var.vel); move to above compute_mass
+
 
     // temperature should be init'd before stress and strain
     initial_temperature(param, var, *var.temperature, *var.radiogenic_source, var.bottom_temperature);
@@ -251,10 +255,8 @@ void restart(const Param& param, Variables& var)
 
     compute_volume(*var.coord, *var.connectivity, *var.volume);
     bin_chkpt.read_array(*var.volume_old, "volume_old");
-    compute_mass(param, var, var.max_vbc_val, *var.volume_n, *var.mass, *var.tmass, *var.hmass, *var.tmp_result);
-
+    compute_mass(param, var, var.max_vbc_val, *var.volume_n, *var.mass, *var.tmass, *var.hmass, *var.ymass, *var.tmp_result);
     compute_shape_fn(var, *var.shpdx, *var.shpdy, *var.shpdz);
-
     create_boundary_normals(var, *var.bnormals, var.edge_vectors);
 
     // Initializing field variables
@@ -289,6 +291,14 @@ void restart(const Param& param, Variables& var)
         initial_weak_zone(param, var, *var.plstrain);
     }
 
+    // For some reason, the following is added by Denis
+    // However, it is not clear why this is needed.
+    if (param.control.has_ATS) {
+        var.dt = compute_dt(param, var);
+        compute_mass(param, var, var.max_vbc_val, *var.volume_n, *var.mass, *var.tmass, *var.hmass, *var.ymass, *var.tmp_result);
+        compute_shape_fn(var, *var.shpdx, *var.shpdy, *var.shpdz);
+    }
+
     phase_changes_init(param, var);
 }
 
@@ -311,9 +321,14 @@ void update_mesh(const Param& param, Variables& var)
 #endif
 
     update_coordinate(var, *var.coord);
-    surface_processes(param, var, *var.coord, *var.stress, *var.strain, *var.strain_rate, \
+
+    if(!param.control.PT_jump)
+    {
+        surface_processes(param, var, *var.coord, *var.stress, *var.strain, *var.strain_rate, \
                       *var.plstrain, *var.volume, *var.volume_n, \
                       var.surfinfo, var.markersets, *var.elemmarkers);
+    }
+    
 //    var.markersets[0]->update_marker_in_elem(var);
 //    var.markersets[0]->create_melt_markers(param.mat.mattype_partial_melting_mantle,var.melt_markers);
 
@@ -332,7 +347,13 @@ void update_mesh(const Param& param, Variables& var)
 #endif
 
     compute_volume(var, *var.volume);
-    compute_mass(param, var, var.max_vbc_val, *var.volume_n, *var.mass, *var.tmass, *var.hmass, *var.tmp_result);
+
+    if (param.control.has_ATS) {
+        var.dt = compute_dt(param, var);
+    }
+
+    compute_mass(param, var, var.max_vbc_val, *var.volume_n, *var.mass, *var.tmass, *var.hmass, *var.ymass, *var.tmp_result);
+
     compute_shape_fn(var, *var.shpdx, *var.shpdy, *var.shpdz);
 #ifdef USE_NPROF
     nvtxRangePop();
@@ -357,7 +378,8 @@ void isostasy_adjustment(const Param &param, Variables &var)
         update_stress(param, var, *var.stress, *var.stressyy, *var.dpressure,
             *var.viscosity, *var.strain, *var.plstrain, *var.delta_plstrain,
             *var.strain_rate,
-            *var.ppressure, *var.dppressure);
+            *var.ppressure, *var.dppressure, *var.vel);
+
         update_force(param, var, *var.force, *var.force_residual, *var.tmp_result);
         update_velocity(var, *var.vel);
 
@@ -412,7 +434,7 @@ void initial_body_force_adjustment(const Param &param, Variables &var)
             update_stress(param, var, *var.stress, *var.stressyy, *var.dpressure,
                 *var.viscosity, *var.strain, *var.plstrain, *var.delta_plstrain,
                 *var.strain_rate,
-                *var.ppressure, *var.dppressure);
+                *var.ppressure, *var.dppressure, *var.vel);
             update_force(param, var, *var.force, *var.force_residual, *var.tmp_result);
             // update_velocity_PT(var, *var.vel);
             update_velocity(var, *var.vel);
@@ -423,7 +445,6 @@ void initial_body_force_adjustment(const Param &param, Variables &var)
             }
             residual_old = var.l2_residual;
         }
-        
     }
 
 #ifdef USE_NPROF
@@ -458,7 +479,7 @@ int main(int argc, const char* argv[])
         init(param, var);
 
         if (param.ic.isostasy_adjustment_time_in_yr > 0) {
-            // output.write_exact(var);
+            // output.write_exact(param, var);
             isostasy_adjustment(param, var);
         }
         if (param.sim.has_initial_checkpoint)
@@ -472,6 +493,8 @@ int main(int argc, const char* argv[])
     var.dt_PT = compute_dt(param, var);
     output.write_exact(param, var);
 
+    // int rheol_type_old = param.mat.rheol_type;
+
     double starting_time = var.time; // var.time & var.steps might be set in restart()
     double starting_step = var.steps;
     int next_regular_frame = 1;  // excluding frames due to output_during_remeshing
@@ -509,7 +532,7 @@ int main(int argc, const char* argv[])
         update_stress(param, var, *var.stress, *var.stressyy, *var.dpressure,
             *var.viscosity, *var.strain, *var.plstrain, *var.delta_plstrain,
             *var.strain_rate,
-            *var.ppressure, *var.dppressure);
+            *var.ppressure, *var.dppressure, *var.vel);
 
 	// Nodal Mixed Discretization For Stress
         if (param.control.is_using_mixed_stress)
@@ -525,7 +548,7 @@ int main(int argc, const char* argv[])
         {   
             // var.dt = compute_dt_PT(param, var);
             if(param.control.has_hydraulic_diffusion) {param.control.has_hydraulic_diffusion = false; hydraulic_diffusion_switch = true;}
-
+            param.control.PT_jump = true;
             for (int pt_step = 0; pt_step < param.control.PT_max_iter; ++pt_step) 
             {
                 apply_vbcs_PT(param, var, *var.vel);
@@ -537,7 +560,7 @@ int main(int argc, const char* argv[])
                 update_stress(param, var, *var.stress, *var.stressyy, *var.dpressure,
                     *var.viscosity, *var.strain, *var.plstrain, *var.delta_plstrain,
                     *var.strain_rate,
-                    *var.ppressure, *var.dppressure);
+                    *var.ppressure, *var.dppressure, *var.vel);
                 update_force(param, var, *var.force, *var.force_residual, *var.tmp_result);
                 // update_velocity_PT(var, *var.vel);
                 update_velocity(var, *var.vel);
@@ -549,9 +572,37 @@ int main(int argc, const char* argv[])
                 }
                 residual_old = var.l2_residual;
                 // var.dt = std::min({var.dt*1.01, dt_copy});
+
+                if (pt_step % param.mesh.quality_check_step_interval == 0) {
+                    if (param.control.has_moving_mesh)
+                    {
+                        int quality_is_bad, bad_quality_index;
+                        quality_is_bad = bad_mesh_quality(param, var, bad_quality_index);
+                        if (quality_is_bad) {
+
+                            if (param.sim.has_output_during_remeshing) {
+                                int64_t time_tmp = get_nanoseconds();
+                                output.write_exact(param, var);
+                                var.func_time.output_time += get_nanoseconds() - time_tmp;
+                            }
+
+                            int64_t time_tmp = get_nanoseconds();
+                            remesh(param, var, quality_is_bad);
+                            var.func_time.remesh_time += get_nanoseconds() - time_tmp;
+
+                            if (param.sim.has_output_during_remeshing) {
+                                int64_t time_tmp = get_nanoseconds();
+                                output.write_exact(param, var);
+                                var.func_time.output_time += get_nanoseconds() - time_tmp;
+                            }
+                        }
+                    }
+                }
             }
             if(hydraulic_diffusion_switch) {param.control.has_hydraulic_diffusion = true;}
             // var.dt = dt_copy;
+            param.control.PT_jump = false;
+
         }
 
 
@@ -577,14 +628,63 @@ int main(int argc, const char* argv[])
                 advect_hydrous_markers(param, var, 10*var.dt,
                                        *var.markersets[var.hydrous_marker_index],
                                        *var.hydrous_elemmarkers);
-
             var.dt = compute_dt(param, var);
         }
 
         if (param.sim.is_outputting_averaged_fields)
             output.average_fields(var);
+        
+        int r = 1;
+        if(param.control.has_ATS)
+        {
+            // r = std::pow(2, log10(var.dt) + 9);
+            // r = std::max(r, 1);
+            // if ((! param.sim.is_outputting_averaged_fields || (var.steps % param.sim.is_outputting_averaged_fields == 0)) &&
+            // // When output_averaged_fields in turned on, the output cannot be
+            // // done at arbitrary time steps.
+            // (((var.steps - starting_step) >= r * param.sim.output_step_interval) ||
+            // ((var.time - starting_time) > param.sim.output_time_interval_in_yr * YEAR2SEC)) ) {
+            //     if (next_regular_frame % param.sim.checkpoint_frame_interval == 0)
+            //     output.write_checkpoint(param, var);
+
+            // int64_t time_tmp = get_nanoseconds();
+            // output.write(var);
+            // var.func_time.output_time += get_nanoseconds() - time_tmp;
+
+            // next_regular_frame ++;
+            // starting_step = var.steps; starting_time = var.time;
+            // }
+
+            if (( (param.sim.output_step_interval != std::numeric_limits<int>::max() &&
+               (var.steps - starting_step) == next_regular_frame * param.sim.output_step_interval)
+              ||
+              (param.sim.output_time_interval_in_yr != std::numeric_limits<double>::max() &&
+               (var.time - starting_time) > next_regular_frame * param.sim.output_time_interval_in_yr * YEAR2SEC)
+              ||
+              (var.max_global_vel_mag > 1e-8)  // **New condition for high velocity**
+             )
+            // time or step output requirements are met
+            &&
+            ((! param.sim.is_outputting_averaged_fields) ||
+                (param.sim.is_outputting_averaged_fields &&
+                    (var.steps % param.mesh.quality_check_step_interval == 0)))
+            // When is_outputting_averaged_fields is turned on, the output cannot be
+            // done at arbitrary time steps.
+            ) {
 
-        if (( (param.sim.output_step_interval != std::numeric_limits<int>::max() &&
+            if (next_regular_frame % param.sim.checkpoint_frame_interval == 0)
+                output.write_checkpoint(param, var);
+
+            int64_t time_tmp = get_nanoseconds();
+            output.write(var);
+            var.func_time.output_time += get_nanoseconds() - time_tmp;
+
+            next_regular_frame ++;
+            }
+        }
+        else
+        {
+            if (( (param.sim.output_step_interval != std::numeric_limits<int>::max() &&
                (var.steps - starting_step) == next_regular_frame * param.sim.output_step_interval)
               ||
               (param.sim.output_time_interval_in_yr != std::numeric_limits<double>::max() &&
@@ -607,12 +707,14 @@ int main(int argc, const char* argv[])
             var.func_time.output_time += get_nanoseconds() - time_tmp;
 
             next_regular_frame ++;
+            }
+
         }
 
         if (var.steps % param.mesh.quality_check_step_interval == 0) {
             if (param.control.has_moving_mesh)
             {
-                int quality_is_bad, bad_quality_index = -1;
+                int quality_is_bad, bad_quality_index;
                 quality_is_bad = bad_mesh_quality(param, var, bad_quality_index);
                 if (quality_is_bad) {
 
diff --git a/examples/rate_and_state_friction/stick_slip.poly b/examples/rate_and_state_friction/stick_slip.poly
new file mode 100644
index 00000000..b6c275b6
--- /dev/null
+++ b/examples/rate_and_state_friction/stick_slip.poly
@@ -0,0 +1,46 @@
+# Mesh layering 
+#  0--------------------7
+#  |    upper layer     |
+#  1--------------------6
+#  | seismogenic zone   |
+#  2--------------------5
+#  |    lower layer     |
+#  3--------------------4
+#
+#### node coordinates ####
+# npoints ndims 0 0
+  8      2     0 0
+# i    xi       yi
+  0    0.e3     0.e3
+  1    0.e3    -4.e3
+  2    0.e3    -6.e3
+  3    0.e3   -10.e3
+  4   100.e3  -10.e3
+  5   100.e3   -6.e3
+  6   100.e3   -4.e3
+  7   100.e3   -0.e3
+
+#### segments ####
+# nsegments 1
+  10        1
+# j  pj0  pj1  bdry_flag
+  0   0    1    1  #  1: left (x0)
+  1   1    2    1
+  2   2    3    1
+  3   3    4    16 # 16: bottom (z0)
+  4   4    5    2  #  2: rigth
+  5   5    6    2
+  6   6    7    2
+  7   7    0    32 # 32: top (z1)
+  8   1    6    0  #  0: not a boundary
+  9   2    5    0
+
+#### holes, must be 0 ####
+0
+#### regions ####
+# nregions
+  3
+# k  xk    zk      mattype  size
+  0 50.e3  -2.e3  2        1.e2  # ratio to the area of regular setting
+  1 50.e3  -5.e3  1        1.e1
+  2 50.e3  -8.e3  0        1.e2
diff --git a/examples/rate_and_state_friction/stick_slip_plot.ipynb b/examples/rate_and_state_friction/stick_slip_plot.ipynb
new file mode 100644
index 00000000..1fd72757
--- /dev/null
+++ b/examples/rate_and_state_friction/stick_slip_plot.ipynb
@@ -0,0 +1,499 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "161f7da9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import necessary libraries\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import re\n",
+    "\n",
+    "import sys\n",
+    "\n",
+    "sys.path.append('../../')\n",
+    "from Dynearthsol import Dynearthsol\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes, mark_inset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "32c83748",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MODEL_NAMES = [\n",
+    "    \"vel_weakening/result\"\n",
+    "]\n",
+    "# Function to read time steps and times from the info file\n",
+    "def read_time_steps(filename):\n",
+    "    data = np.loadtxt(filename, usecols=(0, 2))\n",
+    "    time_steps = data[:, 0].astype(int)\n",
+    "    times = data[:, 1]\n",
+    "    return time_steps, times\n",
+    "\n",
+    "# Function to find the closest node\n",
+    "def find_closest_node(coordinates, target_x, target_depth):\n",
+    "    distances = np.sqrt((coordinates[:, 0] - target_x) ** 2 + (coordinates[:, 1] - target_depth) ** 2)\n",
+    "    return np.argmin(distances)  # Return index of closest node\n",
+    "\n",
+    "# Function to perform numerical calculations for each time step\n",
+    "def numerical(num_steps, model_name):\n",
+    "    des = Dynearthsol(model_name)\n",
+    "    \n",
+    "    # Storage for displacement and velocity at the closest node\n",
+    "    disp_4km = np.zeros([num_steps, 2], dtype=float)\n",
+    "    vel_4km = np.zeros([num_steps, 2], dtype=float)\n",
+    "\n",
+    "    # Read initial coordinates to find closest nodes once\n",
+    "    coordinates0 = des.read_field(0, 'coord0')  # Initial coordinates\n",
+    "    node_4km = find_closest_node(coordinates0, target_x=50000, target_depth=-4000)\n",
+    "\n",
+    "    for time_step in range(num_steps):\n",
+    "        coordinates = des.read_field(time_step, 'coordinate')  # Current coordinates\n",
+    "        vel = des.read_field(time_step, 'velocity')  # Velocity field\n",
+    "        disp = coordinates - coordinates0  # Displacement calculation\n",
+    "\n",
+    "        # Store values for the closest node\n",
+    "        disp_4km[time_step, :] = disp[node_4km]\n",
+    "        vel_4km[time_step, :] = vel[node_4km]\n",
+    "\n",
+    "    return disp_4km, vel_4km\n",
+    "\n",
+    "# Read time steps and numerical times from the info file of one of the models\n",
+    "time_steps, times = read_time_steps(MODEL_NAMES[0] + '.info')\n",
+    "times = times / 86400 / 365.25  # Convert seconds to years\n",
+    "num_steps = len(time_steps)\n",
+    "\n",
+    "# Run numerical analysis\n",
+    "results = [numerical(num_steps, model_name) for model_name in MODEL_NAMES]\n",
+    "disp_4km, vel_4km = [np.squeeze(np.array(arr)) for arr in zip(*results)]\n",
+    "\n",
+    "# Compute magnitude of displacement and velocity\n",
+    "disp_4km_mag = np.linalg.norm(disp_4km, axis=1)\n",
+    "vel_4km_mag = np.linalg.norm(vel_4km, axis=1)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "557eefe4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Define the time window for zooming (in years)\n",
+    "start_time = 2953.335  # Example: Start zoom at 2953.335 years\n",
+    "end_time = 2953.338    # Example: End zoom at 2953.338 years\n",
+    "\n",
+    "# Create a figure with two subplots (panels)\n",
+    "fig, axes = plt.subplots(2, 1, figsize=(12, 10), sharex=True)\n",
+    "\n",
+    "# Plot displacement magnitudes (linear scale)\n",
+    "axes[0].plot(times, disp_4km_mag, label='Displacement at -4 km, x=50 km', color='b', linestyle='-', linewidth=2)\n",
+    "axes[0].set_ylabel('Displacement Magnitude (m)', fontsize=14)\n",
+    "axes[0].legend(fontsize=12)\n",
+    "axes[0].grid(True, linestyle='--', alpha=0.7)\n",
+    "\n",
+    "# axes[0].axvspan(start_time, end_time, color='gray', alpha=0.3, label='Zoomed Region')  # Highlight zoom window\n",
+    "\n",
+    "# # Create an inset zoomed plot for displacement\n",
+    "# axins_disp = inset_axes(axes[0], width=\"45%\", height=\"45%\", loc=\"upper right\")\n",
+    "# axins_disp.plot(times, disp_4km_mag, color='b', linestyle='-', linewidth=2)\n",
+    "# axins_disp.set_xlim(start_time, end_time)  # Zoomed x-axis range\n",
+    "# axins_disp.set_title(\"Zoomed View\", fontsize=12)\n",
+    "# axins_disp.grid(True, linestyle='--', alpha=0.7, which='both')\n",
+    "# axins_disp.set_ylim(85, 86.5)  # **Fixed y-axis range**\n",
+    "# # Draw a connection between the main plot and the zoomed inset\n",
+    "# mark_inset(axes[0], axins_disp, loc1=2, loc2=4, fc=\"none\", ec=\"black\", lw=1)\n",
+    "\n",
+    "# Plot velocity magnitudes (logarithmic scale)\n",
+    "axes[1].plot(times, vel_4km_mag, label='Velocity at -4 km, x=50 km', color='r', linestyle='-', linewidth=2)\n",
+    "axes[1].set_yscale('log')  # Set logarithmic scale for velocity\n",
+    "axes[1].set_xlabel('Time (yr)', fontsize=14)\n",
+    "axes[1].set_ylabel('Velocity Magnitude (m/s)', fontsize=14)\n",
+    "axes[1].legend(fontsize=12)\n",
+    "axes[1].grid(True, linestyle='--', alpha=0.7, which='both')\n",
+    "# axes[1].set_xlim(1000, 1100)  # **Fixed y-axis range**\n",
+    "# axes[1].set_ylim(1e-10, 1e-8)  # **Fixed y-axis range**\n",
+    "# axes[1].set_ylim(1e-14, 1e-0)  # **Fixed y-axis range**\n",
+    "# axes[1].axvspan(start_time-10, end_time+10, color='yellow', label='Zoomed Region')  # Highlight zoom window\n",
+    "\n",
+    "# # Create an inset zoomed plot for velocity\n",
+    "# axins_vel = inset_axes(axes[1], width=\"45%\", height=\"45%\", loc=\"upper right\")\n",
+    "# axins_vel.plot(times, vel_4km_mag, color='r', linestyle='-', linewidth=2)\n",
+    "# axins_vel.set_yscale('log')  # Keep log scale for velocity\n",
+    "# axins_vel.set_xlim(start_time, end_time)  # Zoomed x-axis range\n",
+    "# axins_vel.set_title(\"Zoomed View\", fontsize=12)\n",
+    "# axins_vel.grid(True, linestyle='--', alpha=0.7, which='both')\n",
+    "\n",
+    "# # Draw a connection between the main plot and the zoomed inset\n",
+    "# mark_inset(axes[1], axins_vel, loc1=2, loc2=4, fc=\"none\", ec=\"black\", lw=1)\n",
+    "\n",
+    "# # Improve aesthetics\n",
+    "# for ax in axes:\n",
+    "#     ax.tick_params(axis='both', labelsize=12)\n",
+    "#     ax.spines['top'].set_linewidth(2.5)\n",
+    "#     ax.spines['right'].set_linewidth(2.5)\n",
+    "#     ax.spines['left'].set_linewidth(2.5)\n",
+    "#     ax.spines['bottom'].set_linewidth(2.5)\n",
+    "\n",
+    "# plt.tight_layout()\n",
+    "# plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6cd5671c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MODEL_NAMES = [\n",
+    "    \"vel_neutral/result\"\n",
+    "]\n",
+    "# Function to read time steps and times from the info file\n",
+    "def read_time_steps(filename):\n",
+    "    data = np.loadtxt(filename, usecols=(0, 2))\n",
+    "    time_steps = data[:, 0].astype(int)\n",
+    "    times = data[:, 1]\n",
+    "    return time_steps, times\n",
+    "\n",
+    "# Function to find the closest node\n",
+    "def find_closest_node(coordinates, target_x, target_depth):\n",
+    "    distances = np.sqrt((coordinates[:, 0] - target_x) ** 2 + (coordinates[:, 1] - target_depth) ** 2)\n",
+    "    return np.argmin(distances)  # Return index of closest node\n",
+    "\n",
+    "# Function to perform numerical calculations for each time step\n",
+    "def numerical(num_steps, model_name):\n",
+    "    des = Dynearthsol(model_name)\n",
+    "    \n",
+    "    # Storage for displacement and velocity at the closest node\n",
+    "    disp_4km = np.zeros([num_steps, 2], dtype=float)\n",
+    "    vel_4km = np.zeros([num_steps, 2], dtype=float)\n",
+    "\n",
+    "    # Read initial coordinates to find closest nodes once\n",
+    "    coordinates0 = des.read_field(0, 'coord0')  # Initial coordinates\n",
+    "    node_4km = find_closest_node(coordinates0, target_x=50000, target_depth=-4000)\n",
+    "\n",
+    "    for time_step in range(num_steps):\n",
+    "        coordinates = des.read_field(time_step, 'coordinate')  # Current coordinates\n",
+    "        vel = des.read_field(time_step, 'velocity')  # Velocity field\n",
+    "        disp = coordinates - coordinates0  # Displacement calculation\n",
+    "\n",
+    "        # Store values for the closest node\n",
+    "        disp_4km[time_step, :] = disp[node_4km]\n",
+    "        vel_4km[time_step, :] = vel[node_4km]\n",
+    "\n",
+    "    return disp_4km, vel_4km\n",
+    "\n",
+    "# Read time steps and numerical times from the info file of one of the models\n",
+    "time_steps, times = read_time_steps(MODEL_NAMES[0] + '.info')\n",
+    "times = times / 86400 / 365.25  # Convert seconds to years\n",
+    "num_steps = len(time_steps)\n",
+    "\n",
+    "# Run numerical analysis\n",
+    "results = [numerical(num_steps, model_name) for model_name in MODEL_NAMES]\n",
+    "disp_4km, vel_4km = [np.squeeze(np.array(arr)) for arr in zip(*results)]\n",
+    "\n",
+    "# Compute magnitude of displacement and velocity\n",
+    "disp_4km_mag = np.linalg.norm(disp_4km, axis=1)\n",
+    "vel_4km_mag = np.linalg.norm(vel_4km, axis=1)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "5cd685b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Define the time window for zooming (in years)\n",
+    "start_time = 2953.335  # Example: Start zoom at 2953.335 years\n",
+    "end_time = 2953.338    # Example: End zoom at 2953.338 years\n",
+    "\n",
+    "# Create a figure with two subplots (panels)\n",
+    "fig, axes = plt.subplots(2, 1, figsize=(12, 10), sharex=True)\n",
+    "\n",
+    "# Plot displacement magnitudes (linear scale)\n",
+    "axes[0].plot(times, disp_4km_mag, label='Displacement at -4 km, x=50 km', color='b', linestyle='-', linewidth=2)\n",
+    "axes[0].set_ylabel('Displacement Magnitude (m)', fontsize=14)\n",
+    "axes[0].legend(fontsize=12)\n",
+    "axes[0].grid(True, linestyle='--', alpha=0.7)\n",
+    "\n",
+    "# axes[0].axvspan(start_time, end_time, color='gray', alpha=0.3, label='Zoomed Region')  # Highlight zoom window\n",
+    "\n",
+    "# # Create an inset zoomed plot for displacement\n",
+    "# axins_disp = inset_axes(axes[0], width=\"45%\", height=\"45%\", loc=\"upper right\")\n",
+    "# axins_disp.plot(times, disp_4km_mag, color='b', linestyle='-', linewidth=2)\n",
+    "# axins_disp.set_xlim(start_time, end_time)  # Zoomed x-axis range\n",
+    "# axins_disp.set_title(\"Zoomed View\", fontsize=12)\n",
+    "# axins_disp.grid(True, linestyle='--', alpha=0.7, which='both')\n",
+    "# axins_disp.set_ylim(85, 86.5)  # **Fixed y-axis range**\n",
+    "# # Draw a connection between the main plot and the zoomed inset\n",
+    "# mark_inset(axes[0], axins_disp, loc1=2, loc2=4, fc=\"none\", ec=\"black\", lw=1)\n",
+    "\n",
+    "# Plot velocity magnitudes (logarithmic scale)\n",
+    "axes[1].plot(times, vel_4km_mag, label='Velocity at -4 km, x=50 km', color='r', linestyle='-', linewidth=2)\n",
+    "axes[1].set_yscale('log')  # Set logarithmic scale for velocity\n",
+    "axes[1].set_xlabel('Time (yr)', fontsize=14)\n",
+    "axes[1].set_ylabel('Velocity Magnitude (m/s)', fontsize=14)\n",
+    "axes[1].legend(fontsize=12)\n",
+    "axes[1].grid(True, linestyle='--', alpha=0.7, which='both')\n",
+    "# axes[1].set_ylim(1e-10, 1e-8)  # **Fixed y-axis range**\n",
+    "# axes[1].set_ylim(1e-14, 1e-0)  # **Fixed y-axis range**\n",
+    "# axes[1].axvspan(start_time-10, end_time+10, color='yellow', label='Zoomed Region')  # Highlight zoom window\n",
+    "\n",
+    "# # Create an inset zoomed plot for velocity\n",
+    "# axins_vel = inset_axes(axes[1], width=\"45%\", height=\"45%\", loc=\"upper right\")\n",
+    "# axins_vel.plot(times, vel_4km_mag, color='r', linestyle='-', linewidth=2)\n",
+    "# axins_vel.set_yscale('log')  # Keep log scale for velocity\n",
+    "# axins_vel.set_xlim(start_time, end_time)  # Zoomed x-axis range\n",
+    "# axins_vel.set_title(\"Zoomed View\", fontsize=12)\n",
+    "# axins_vel.grid(True, linestyle='--', alpha=0.7, which='both')\n",
+    "\n",
+    "# # Draw a connection between the main plot and the zoomed inset\n",
+    "# mark_inset(axes[1], axins_vel, loc1=2, loc2=4, fc=\"none\", ec=\"black\", lw=1)\n",
+    "\n",
+    "# # Improve aesthetics\n",
+    "# for ax in axes:\n",
+    "#     ax.tick_params(axis='both', labelsize=12)\n",
+    "#     ax.spines['top'].set_linewidth(2.5)\n",
+    "#     ax.spines['right'].set_linewidth(2.5)\n",
+    "#     ax.spines['left'].set_linewidth(2.5)\n",
+    "#     ax.spines['bottom'].set_linewidth(2.5)\n",
+    "\n",
+    "# plt.tight_layout()\n",
+    "# plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0e87cea1",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "vel_strengthening/result.info not found.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[6], line 40\u001b[0m\n\u001b[1;32m     37\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m disp_4km, vel_4km\n\u001b[1;32m     39\u001b[0m \u001b[38;5;66;03m# Read time steps and numerical times from the info file of one of the models\u001b[39;00m\n\u001b[0;32m---> 40\u001b[0m time_steps, times \u001b[38;5;241m=\u001b[39m \u001b[43mread_time_steps\u001b[49m\u001b[43m(\u001b[49m\u001b[43mMODEL_NAMES\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m.info\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     41\u001b[0m times \u001b[38;5;241m=\u001b[39m times \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m86400\u001b[39m \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m365.25\u001b[39m  \u001b[38;5;66;03m# Convert seconds to years\u001b[39;00m\n\u001b[1;32m     42\u001b[0m num_steps \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(time_steps)\n",
+      "Cell \u001b[0;32mIn[6], line 6\u001b[0m, in \u001b[0;36mread_time_steps\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mread_time_steps\u001b[39m(filename):\n\u001b[0;32m----> 6\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloadtxt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43musecols\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      7\u001b[0m     time_steps \u001b[38;5;241m=\u001b[39m data[:, \u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mint\u001b[39m)\n\u001b[1;32m      8\u001b[0m     times \u001b[38;5;241m=\u001b[39m data[:, \u001b[38;5;241m1\u001b[39m]\n",
+      "File \u001b[0;32m~/.local/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py:1397\u001b[0m, in \u001b[0;36mloadtxt\u001b[0;34m(fname, dtype, comments, delimiter, converters, skiprows, usecols, unpack, ndmin, encoding, max_rows, quotechar, like)\u001b[0m\n\u001b[1;32m   1394\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(delimiter, \u001b[38;5;28mbytes\u001b[39m):\n\u001b[1;32m   1395\u001b[0m     delimiter \u001b[38;5;241m=\u001b[39m delimiter\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlatin1\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m-> 1397\u001b[0m arr \u001b[38;5;241m=\u001b[39m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcomment\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcomment\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdelimiter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdelimiter\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1398\u001b[0m \u001b[43m            \u001b[49m\u001b[43mconverters\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconverters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskiplines\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskiprows\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43musecols\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43musecols\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1399\u001b[0m \u001b[43m            \u001b[49m\u001b[43munpack\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43munpack\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mndmin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mndmin\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1400\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmax_rows\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_rows\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mquote\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquotechar\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1402\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m arr\n",
+      "File \u001b[0;32m~/.local/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py:1012\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(fname, delimiter, comment, quote, imaginary_unit, usecols, skiplines, max_rows, converters, ndmin, unpack, dtype, encoding)\u001b[0m\n\u001b[1;32m   1010\u001b[0m     fname \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mfspath(fname)\n\u001b[1;32m   1011\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(fname, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m-> 1012\u001b[0m     fh \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_datasource\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mrt\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1013\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m encoding \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   1014\u001b[0m         encoding \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(fh, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mencoding\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlatin1\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "File \u001b[0;32m~/.local/lib/python3.12/site-packages/numpy/lib/_datasource.py:192\u001b[0m, in \u001b[0;36mopen\u001b[0;34m(path, mode, destpath, encoding, newline)\u001b[0m\n\u001b[1;32m    155\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    156\u001b[0m \u001b[38;5;124;03mOpen `path` with `mode` and return the file object.\u001b[39;00m\n\u001b[1;32m    157\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    188\u001b[0m \n\u001b[1;32m    189\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    191\u001b[0m ds \u001b[38;5;241m=\u001b[39m DataSource(destpath)\n\u001b[0;32m--> 192\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnewline\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/.local/lib/python3.12/site-packages/numpy/lib/_datasource.py:529\u001b[0m, in \u001b[0;36mDataSource.open\u001b[0;34m(self, path, mode, encoding, newline)\u001b[0m\n\u001b[1;32m    526\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _file_openers[ext](found, mode\u001b[38;5;241m=\u001b[39mmode,\n\u001b[1;32m    527\u001b[0m                               encoding\u001b[38;5;241m=\u001b[39mencoding, newline\u001b[38;5;241m=\u001b[39mnewline)\n\u001b[1;32m    528\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 529\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not found.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "\u001b[0;31mFileNotFoundError\u001b[0m: vel_strengthening/result.info not found."
+     ]
+    }
+   ],
+   "source": [
+    "MODEL_NAMES = [\n",
+    "    \"vel_strengthening/result\"\n",
+    "]\n",
+    "# Function to read time steps and times from the info file\n",
+    "def read_time_steps(filename):\n",
+    "    data = np.loadtxt(filename, usecols=(0, 2))\n",
+    "    time_steps = data[:, 0].astype(int)\n",
+    "    times = data[:, 1]\n",
+    "    return time_steps, times\n",
+    "\n",
+    "# Function to find the closest node\n",
+    "def find_closest_node(coordinates, target_x, target_depth):\n",
+    "    distances = np.sqrt((coordinates[:, 0] - target_x) ** 2 + (coordinates[:, 1] - target_depth) ** 2)\n",
+    "    return np.argmin(distances)  # Return index of closest node\n",
+    "\n",
+    "# Function to perform numerical calculations for each time step\n",
+    "def numerical(num_steps, model_name):\n",
+    "    des = Dynearthsol(model_name)\n",
+    "    \n",
+    "    # Storage for displacement and velocity at the closest node\n",
+    "    disp_4km = np.zeros([num_steps, 2], dtype=float)\n",
+    "    vel_4km = np.zeros([num_steps, 2], dtype=float)\n",
+    "\n",
+    "    # Read initial coordinates to find closest nodes once\n",
+    "    coordinates0 = des.read_field(0, 'coord0')  # Initial coordinates\n",
+    "    node_4km = find_closest_node(coordinates0, target_x=50000, target_depth=-4000)\n",
+    "\n",
+    "    for time_step in range(num_steps):\n",
+    "        coordinates = des.read_field(time_step, 'coordinate')  # Current coordinates\n",
+    "        vel = des.read_field(time_step, 'velocity')  # Velocity field\n",
+    "        disp = coordinates - coordinates0  # Displacement calculation\n",
+    "\n",
+    "        # Store values for the closest node\n",
+    "        disp_4km[time_step, :] = disp[node_4km]\n",
+    "        vel_4km[time_step, :] = vel[node_4km]\n",
+    "\n",
+    "    return disp_4km, vel_4km\n",
+    "\n",
+    "# Read time steps and numerical times from the info file of one of the models\n",
+    "time_steps, times = read_time_steps(MODEL_NAMES[0] + '.info')\n",
+    "times = times / 86400 / 365.25  # Convert seconds to years\n",
+    "num_steps = len(time_steps)\n",
+    "\n",
+    "# Run numerical analysis\n",
+    "results = [numerical(num_steps, model_name) for model_name in MODEL_NAMES]\n",
+    "disp_4km, vel_4km = [np.squeeze(np.array(arr)) for arr in zip(*results)]\n",
+    "\n",
+    "# Compute magnitude of displacement and velocity\n",
+    "disp_4km_mag = np.linalg.norm(disp_4km, axis=1)\n",
+    "vel_4km_mag = np.linalg.norm(vel_4km, axis=1)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dd065fc0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000.0, 1100.0)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Define the time window for zooming (in years)\n",
+    "start_time = 2953.335  # Example: Start zoom at 2953.335 years\n",
+    "end_time = 2953.338    # Example: End zoom at 2953.338 years\n",
+    "\n",
+    "# Create a figure with two subplots (panels)\n",
+    "fig, axes = plt.subplots(2, 1, figsize=(12, 10), sharex=True)\n",
+    "\n",
+    "# Plot displacement magnitudes (linear scale)\n",
+    "axes[0].plot(times, disp_4km_mag, label='Displacement at -4 km, x=50 km', color='b', linestyle='-', linewidth=2)\n",
+    "axes[0].set_ylabel('Displacement Magnitude (m)', fontsize=14)\n",
+    "axes[0].legend(fontsize=12)\n",
+    "axes[0].grid(True, linestyle='--', alpha=0.7)\n",
+    "\n",
+    "# axes[0].axvspan(start_time, end_time, color='gray', alpha=0.3, label='Zoomed Region')  # Highlight zoom window\n",
+    "\n",
+    "# # Create an inset zoomed plot for displacement\n",
+    "# axins_disp = inset_axes(axes[0], width=\"45%\", height=\"45%\", loc=\"upper right\")\n",
+    "# axins_disp.plot(times, disp_4km_mag, color='b', linestyle='-', linewidth=2)\n",
+    "# axins_disp.set_xlim(start_time, end_time)  # Zoomed x-axis range\n",
+    "# axins_disp.set_title(\"Zoomed View\", fontsize=12)\n",
+    "# axins_disp.grid(True, linestyle='--', alpha=0.7, which='both')\n",
+    "# axins_disp.set_ylim(85, 86.5)  # **Fixed y-axis range**\n",
+    "# # Draw a connection between the main plot and the zoomed inset\n",
+    "# mark_inset(axes[0], axins_disp, loc1=2, loc2=4, fc=\"none\", ec=\"black\", lw=1)\n",
+    "\n",
+    "# Plot velocity magnitudes (logarithmic scale)\n",
+    "axes[1].plot(times, vel_4km_mag, label='Velocity at -4 km, x=50 km', color='r', linestyle='-', linewidth=2)\n",
+    "axes[1].set_yscale('log')  # Set logarithmic scale for velocity\n",
+    "axes[1].set_xlabel('Time (yr)', fontsize=14)\n",
+    "axes[1].set_ylabel('Velocity Magnitude (m/s)', fontsize=14)\n",
+    "axes[1].legend(fontsize=12)\n",
+    "axes[1].grid(True, linestyle='--', alpha=0.7, which='both')\n",
+    "# axes[1].set_ylim(6e-10, 1.1e-9)  # **Fixed y-axis range**\n",
+    "# axes[1].set_xlim(1000, 1100)  # **Fixed y-axis range**\n",
+    "# axes[1].set_ylim(1e-14, 1e-0)  # **Fixed y-axis range**\n",
+    "# axes[1].axvspan(start_time-10, end_time+10, color='yellow', label='Zoomed Region')  # Highlight zoom window\n",
+    "\n",
+    "# # Create an inset zoomed plot for velocity\n",
+    "# axins_vel = inset_axes(axes[1], width=\"45%\", height=\"45%\", loc=\"upper right\")\n",
+    "# axins_vel.plot(times, vel_4km_mag, color='r', linestyle='-', linewidth=2)\n",
+    "# axins_vel.set_yscale('log')  # Keep log scale for velocity\n",
+    "# axins_vel.set_xlim(start_time, end_time)  # Zoomed x-axis range\n",
+    "# axins_vel.set_title(\"Zoomed View\", fontsize=12)\n",
+    "# axins_vel.grid(True, linestyle='--', alpha=0.7, which='both')\n",
+    "\n",
+    "# # Draw a connection between the main plot and the zoomed inset\n",
+    "# mark_inset(axes[1], axins_vel, loc1=2, loc2=4, fc=\"none\", ec=\"black\", lw=1)\n",
+    "\n",
+    "# # Improve aesthetics\n",
+    "# for ax in axes:\n",
+    "#     ax.tick_params(axis='both', labelsize=12)\n",
+    "#     ax.spines['top'].set_linewidth(2.5)\n",
+    "#     ax.spines['right'].set_linewidth(2.5)\n",
+    "#     ax.spines['left'].set_linewidth(2.5)\n",
+    "#     ax.spines['bottom'].set_linewidth(2.5)\n",
+    "\n",
+    "# plt.tight_layout()\n",
+    "# plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ad46a695",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/rate_and_state_friction/stick_slip_plot_temp.ipynb b/examples/rate_and_state_friction/stick_slip_plot_temp.ipynb
new file mode 100644
index 00000000..4f8fc00d
--- /dev/null
+++ b/examples/rate_and_state_friction/stick_slip_plot_temp.ipynb
@@ -0,0 +1,211 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "161f7da9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import necessary libraries\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import re\n",
+    "\n",
+    "import sys\n",
+    "\n",
+    "sys.path.append('../../')\n",
+    "from Dynearthsol import Dynearthsol\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes, mark_inset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0e87cea1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.00000000e+00 4.10498897e-01 7.66109907e-01 ... 2.47063243e+03\n",
+      " 2.47063497e+03 2.47063750e+03]\n"
+     ]
+    }
+   ],
+   "source": [
+    "MODEL_NAMES = [\n",
+    "    \"vel_weakening/result\"\n",
+    "]\n",
+    "# Function to read time steps and times from the info file\n",
+    "def read_time_steps(filename):\n",
+    "    data = np.loadtxt(filename, usecols=(0, 2))\n",
+    "    time_steps = data[:, 0].astype(int)\n",
+    "    times = data[:, 1]\n",
+    "    return time_steps, times\n",
+    "\n",
+    "# Function to find the closest node\n",
+    "def find_closest_node(coordinates, target_x, target_depth):\n",
+    "    distances = np.sqrt((coordinates[:, 0] - target_x) ** 2 + (coordinates[:, 1] - target_depth) ** 2)\n",
+    "    return np.argmin(distances)  # Return index of closest node\n",
+    "\n",
+    "# Function to perform numerical calculations for each time step\n",
+    "def numerical(num_steps, model_name):\n",
+    "    des = Dynearthsol(model_name)\n",
+    "    \n",
+    "    # Storage for displacement and velocity at the closest node\n",
+    "    disp_4km = np.zeros([num_steps, 2], dtype=float)\n",
+    "    vel_4km = np.zeros([num_steps, 2], dtype=float)\n",
+    "\n",
+    "    # Read initial coordinates to find closest nodes once\n",
+    "    coordinates0 = des.read_field(0, 'coord0')  # Initial coordinates\n",
+    "    node_4km = find_closest_node(coordinates0, target_x=50000, target_depth=-4050)\n",
+    "\n",
+    "    for time_step in range(num_steps):\n",
+    "        coordinates = des.read_field(time_step, 'coordinate')  # Current coordinates\n",
+    "        vel = des.read_field(time_step, 'velocity')  # Velocity field\n",
+    "        disp = coordinates - coordinates0  # Displacement calculation\n",
+    "\n",
+    "        # Store values for the closest node\n",
+    "        disp_4km[time_step, :] = disp[node_4km]\n",
+    "        vel_4km[time_step, :] = vel[node_4km]\n",
+    "\n",
+    "    return disp_4km, vel_4km\n",
+    "\n",
+    "# Read time steps and numerical times from the info file of one of the models\n",
+    "time_steps, times = read_time_steps(MODEL_NAMES[0] + '.info')\n",
+    "times = times / 86400 / 365.25  # Convert seconds to years\n",
+    "\n",
+    "print(times)\n",
+    "num_steps = len(time_steps)\n",
+    "\n",
+    "# Run numerical analysis\n",
+    "results = [numerical(num_steps, model_name) for model_name in MODEL_NAMES]\n",
+    "disp_4km, vel_4km = [np.squeeze(np.array(arr)) for arr in zip(*results)]\n",
+    "\n",
+    "# Compute magnitude of displacement and velocity\n",
+    "disp_4km_mag = np.linalg.norm(disp_4km, axis=1)\n",
+    "vel_4km_mag = np.linalg.norm(vel_4km, axis=1)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "dd065fc0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x1800 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1800 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Define zoom intervals\n",
+    "zoom_windows = [(0, 1000), (1100, 1800), (1900, 2200), (2250, 2350), (2400, 2450)]\n",
+    "\n",
+    "# --- Displacement Plot ---\n",
+    "fig_disp, axes_disp = plt.subplots(6, 1, figsize=(12, 18))  # NOT sharex\n",
+    "fig_disp.suptitle(\"Displacement Magnitude: Full View and Zoomed Windows\", fontsize=16)\n",
+    "\n",
+    "# Full time range\n",
+    "axes_disp[0].plot(times, disp_4km_mag, color='b', linewidth=1.5)\n",
+    "axes_disp[0].set_ylabel(\"Disp (m)\", fontsize=12)\n",
+    "axes_disp[0].set_title(\"Full Time Range\", fontsize=12)\n",
+    "for (start, end) in zoom_windows:\n",
+    "    axes_disp[0].axvspan(start, end, color='gray', alpha=0.3)\n",
+    "axes_disp[0].grid(True, linestyle='--', alpha=0.5)\n",
+    "\n",
+    "# Zoom panels\n",
+    "for i, (start, end) in enumerate(zoom_windows):\n",
+    "    ax = axes_disp[i + 1]\n",
+    "    ax.plot(times, disp_4km_mag, color='b', linewidth=1.0)\n",
+    "    ax.set_xlim(start, end)\n",
+    "    ax.set_ylabel(\"Disp (m)\", fontsize=10)\n",
+    "    ax.set_title(f\"Zoom {start}-{end} yr\", fontsize=10)\n",
+    "    ax.grid(True, linestyle='--', alpha=0.5)\n",
+    "    ax.tick_params(labelsize=9)\n",
+    "axes_disp[-1].set_xlabel(\"Time (yr)\", fontsize=12)\n",
+    "\n",
+    "plt.tight_layout(rect=[0, 0, 1, 0.96])\n",
+    "plt.savefig(\"displacement_magnitude.png\", dpi=300)\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "# --- Velocity Plot ---\n",
+    "fig_vel, axes_vel = plt.subplots(6, 1, figsize=(12, 18))  # NOT sharex\n",
+    "fig_vel.suptitle(\"Velocity Magnitude (Log): Full View and Zoomed Windows\", fontsize=16)\n",
+    "\n",
+    "# Full time range\n",
+    "axes_vel[0].plot(times, vel_4km_mag, color='r', linewidth=1.5)\n",
+    "axes_vel[0].set_yscale('log')\n",
+    "axes_vel[0].set_ylabel(\"Vel (m/s)\", fontsize=12)\n",
+    "axes_vel[0].set_title(\"Full Time Range\", fontsize=12)\n",
+    "for (start, end) in zoom_windows:\n",
+    "    axes_vel[0].axvspan(start, end, color='yellow', alpha=0.3)\n",
+    "axes_vel[0].grid(True, linestyle='--', alpha=0.5, which='both')\n",
+    "\n",
+    "# Zoom panels\n",
+    "for i, (start, end) in enumerate(zoom_windows):\n",
+    "    ax = axes_vel[i + 1]\n",
+    "    ax.plot(times, vel_4km_mag, color='r', linewidth=1.0)\n",
+    "    ax.set_xlim(start, end)\n",
+    "    ax.set_yscale('log')\n",
+    "    ax.set_ylabel(\"Vel (m/s)\", fontsize=10)\n",
+    "    ax.set_title(f\"Zoom {start}-{end} yr\", fontsize=10)\n",
+    "    ax.grid(True, linestyle='--', alpha=0.5, which='both')\n",
+    "    ax.tick_params(labelsize=9)\n",
+    "axes_vel[-1].set_xlabel(\"Time (yr)\", fontsize=12)\n",
+    "\n",
+    "plt.tight_layout(rect=[0, 0, 1, 0.96])\n",
+    "plt.savefig(\"velocity_magnitude.png\", dpi=300)\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/rate_and_state_friction/vel_neutral/rsf_tri_layer_shear_test_vel_neutral.cfg b/examples/rate_and_state_friction/vel_neutral/rsf_tri_layer_shear_test_vel_neutral.cfg
new file mode 100644
index 00000000..1127f237
--- /dev/null
+++ b/examples/rate_and_state_friction/vel_neutral/rsf_tri_layer_shear_test_vel_neutral.cfg
@@ -0,0 +1,82 @@
+[sim]
+modelname = result
+#max_steps = 1000
+max_time_in_yr = 2500
+output_time_interval_in_yr = 50
+output_step_interval = 10
+
+has_output_during_remeshing = no
+is_outputting_averaged_fields = no
+
+[mesh]
+meshing_option = 91
+poly_filename = ../stick_slip.poly
+meshing_sediment = yes
+sediment_size = 2.e1
+
+xlength = 100e3
+ylength = 100e3
+zlength = 10e3
+resolution = 1e2
+largest_size = 1.e5
+smallest_size = 1.e0
+
+quality_check_step_interval = 500
+
+min_angle = 30.
+min_quality = 0.2
+max_boundary_distortion = 1e0
+remeshing_option = 11
+is_discarding_internal_segments = yes
+
+[control]
+surface_process_option = 1
+surface_diffusivity = 1e-20
+
+dt_fraction = 1.0
+inertial_scaling = 1e5
+
+has_ATS = true
+#damping_option=4
+[bc]
+vbc_x0 = 0
+vbc_x1 = 0
+# vbc_val_x0 = -1e-9
+# vbc_val_x1 = 1e-9
+
+vbc_y0 = 1
+vbc_y1 = 1
+vbc_val_y0 = 0
+vbc_val_y1 = 0
+
+vbc_z0 = 3
+vbc_z1 = 4
+vbc_val_z0 = 0
+vbc_val_z1 = 1e-9
+
+has_winkler_foundation = false
+has_water_loading = no
+
+surface_temperature = 273
+mantle_temperature = 273
+
+[ic]
+weakzone_plstrain = 0.0
+
+[mat]
+rheology_type = elasto-plastic-rsf
+num_materials = 3
+rho0 = [2700]
+alpha = [0]
+bulk_modulus = [50e9]
+shear_modulus = [30e9]
+pls0 = [0]
+pls1 = [0.5]
+cohesion0 = [1e50,4e6,1e50]
+cohesion1 = [1e50,4e6,1e50]
+friction_angle0 = [30]
+friction_angle1 = [30]
+
+direct_a = [0.02,0.02,0.02]
+evolution_b = [0.025,0.02,0.025] 
+min_viscosity = 1e24
diff --git a/examples/rate_and_state_friction/vel_strengthening/rsf_tri_layer_shear_test_vel_strengthening.cfg b/examples/rate_and_state_friction/vel_strengthening/rsf_tri_layer_shear_test_vel_strengthening.cfg
new file mode 100644
index 00000000..74371cd3
--- /dev/null
+++ b/examples/rate_and_state_friction/vel_strengthening/rsf_tri_layer_shear_test_vel_strengthening.cfg
@@ -0,0 +1,82 @@
+[sim]
+modelname = result
+#max_steps = 1000
+max_time_in_yr = 2500
+output_time_interval_in_yr = 50
+output_step_interval = 10
+
+has_output_during_remeshing = no
+is_outputting_averaged_fields = no
+
+[mesh]
+meshing_option = 91
+poly_filename = ../stick_slip.poly
+meshing_sediment = yes
+sediment_size = 2.e1
+
+xlength = 100e3
+ylength = 100e3
+zlength = 10e3
+resolution = 1e2
+largest_size = 1.e5
+smallest_size = 1.e0
+
+quality_check_step_interval = 500
+
+min_angle = 30.
+min_quality = 0.2
+max_boundary_distortion = 1e0
+remeshing_option = 11
+is_discarding_internal_segments = yes
+
+[control]
+surface_process_option = 1
+surface_diffusivity = 1e-20
+
+dt_fraction = 1.0
+inertial_scaling = 1e5
+
+has_ATS = true
+#damping_option=4
+[bc]
+vbc_x0 = 0
+vbc_x1 = 0
+# vbc_val_x0 = -1e-9
+# vbc_val_x1 = 1e-9
+
+vbc_y0 = 1
+vbc_y1 = 1
+vbc_val_y0 = 0
+vbc_val_y1 = 0
+
+vbc_z0 = 3
+vbc_z1 = 4
+vbc_val_z0 = 0
+vbc_val_z1 = 1e-9
+
+has_winkler_foundation = false
+has_water_loading = no
+
+surface_temperature = 273
+mantle_temperature = 273
+
+[ic]
+weakzone_plstrain = 0.0
+
+[mat]
+rheology_type = elasto-plastic-rsf
+num_materials = 3
+rho0 = [2700]
+alpha = [0]
+bulk_modulus = [50e9]
+shear_modulus = [30e9]
+pls0 = [0]
+pls1 = [0.5]
+cohesion0 = [1e50,4e6,1e50]
+cohesion1 = [1e50,4e6,1e50]
+friction_angle0 = [30]
+friction_angle1 = [30]
+
+direct_a = [0.02,0.02,0.02]
+evolution_b = [0.025,0.0205,0.025] 
+min_viscosity = 1e24
diff --git a/examples/rate_and_state_friction/vel_weakening/rsf_tri_layer_shear_test_vel_weakening.cfg b/examples/rate_and_state_friction/vel_weakening/rsf_tri_layer_shear_test_vel_weakening.cfg
new file mode 100644
index 00000000..ea20a588
--- /dev/null
+++ b/examples/rate_and_state_friction/vel_weakening/rsf_tri_layer_shear_test_vel_weakening.cfg
@@ -0,0 +1,82 @@
+[sim]
+modelname = result
+#max_steps = 1000
+max_time_in_yr = 2500
+output_time_interval_in_yr = 50
+output_step_interval = 10
+
+has_output_during_remeshing = no
+is_outputting_averaged_fields = no
+
+[mesh]
+meshing_option = 91
+poly_filename = ../stick_slip.poly
+meshing_sediment = yes
+sediment_size = 2.e1
+
+xlength = 100e3
+ylength = 100e3
+zlength = 10e3
+resolution = 1e2
+largest_size = 1.e5
+smallest_size = 1.e0
+
+quality_check_step_interval = 500
+
+min_angle = 30.
+min_quality = 0.2
+max_boundary_distortion = 1e0
+remeshing_option = 11
+is_discarding_internal_segments = yes
+
+[control]
+surface_process_option = 1
+surface_diffusivity = 1e-20
+
+dt_fraction = 1.0
+inertial_scaling = 1e5
+
+has_ATS = true
+#damping_option=4
+[bc]
+vbc_x0 = 0
+vbc_x1 = 0
+# vbc_val_x0 = -1e-9
+# vbc_val_x1 = 1e-9
+
+vbc_y0 = 1
+vbc_y1 = 1
+vbc_val_y0 = 0
+vbc_val_y1 = 0
+
+vbc_z0 = 3
+vbc_z1 = 4
+vbc_val_z0 = 0
+vbc_val_z1 = 1e-9
+
+has_winkler_foundation = false
+has_water_loading = no
+
+surface_temperature = 273
+mantle_temperature = 273
+
+[ic]
+weakzone_plstrain = 0.0
+
+[mat]
+rheology_type = elasto-plastic-rsf
+num_materials = 3
+rho0 = [2700]
+alpha = [0]
+bulk_modulus = [50e9]
+shear_modulus = [30e9]
+pls0 = [0]
+pls1 = [0.5]
+cohesion0 = [1e50,4e6,1e50]
+cohesion1 = [1e50,4e6,1e50]
+friction_angle0 = [30]
+friction_angle1 = [30]
+
+direct_a = [0.02,0.02,0.02]
+evolution_b = [0.025,0.025,0.025] 
+min_viscosity = 1e24
diff --git a/fields.cxx b/fields.cxx
index c129a8b5..5e646b5e 100644
--- a/fields.cxx
+++ b/fields.cxx
@@ -21,7 +21,7 @@ void allocate_variables(const Param &param, Variables& var)
     var.mass = new double_vec(n);
     var.tmass = new double_vec(n);
     var.hmass = new double_vec(n);
-
+    var.ymass = new double_vec(n);
     var.edvoldt = new double_vec(e);
 
 //    var.marker_in_elem = new int_vec2D(e);
@@ -41,6 +41,22 @@ void allocate_variables(const Param &param, Variables& var)
         var.old_mean_stress = new double_vec(e, 0);
         // var.stress_old = new tensor_t(e, 0);
         var.radiogenic_source = new double_vec(e, 0);
+
+//         // these fields are from Denis' code
+//         var.MAX_shear = new double_vec(e);
+//         var.MAX_shear_0 = new double_vec(e);
+//         var.CL = new double_vec(e);
+//         var.dl_min = new double_vec(e);
+//         var.maxv = new double_vec(e);
+//         var.strain_energy = new double_vec(e);
+//         var.kinetic_energy = new double_vec(e);
+// #ifdef RS
+//         var.state1 = new double_vec(e);
+//         var.slip_velocity = new double_vec(e);
+//         var.friction_coefficient = new double_vec(e);
+//         var.RS_shear = new double_vec(e);
+//         var.Failure_mode = new double_vec(e);
+// #endif
     }
 
     var.ntmp = new double_vec(n);
@@ -83,7 +99,9 @@ void reallocate_variables(const Param& param, Variables& var)
 
     delete var.hmass;
     var.hmass = new double_vec(n);
-
+    delete var.ymass;
+    var.ymass = new double_vec(n);
+  
     delete var.edvoldt;
     var.edvoldt = new double_vec(e);
 
@@ -458,6 +476,30 @@ static void apply_damping(const Param& param, const Variables& var, array_t& for
         break;
     case 4:
         // rayleigh damping
+#ifdef GPP1X
+        #pragma omp parallel for default(none) \
+            shared(var, param, force, small_vel)
+#else
+        #pragma omp parallel for default(none) \
+        shared(var, param, force, small_vel)
+#endif
+        #pragma acc parallel loop
+        for (int i=0; i<var.nnode; ++i) {
+            double mass = (*var.mass)[i];
+            double young = (*var.ymass)[i];
+            double critical_coeff = 2.0 * std::sqrt(mass * young);
+            
+            for (int i=0; i<var.nnode; ++i) {
+                for (int j=0;j<NDIMS;j++)
+                    if (std::fabs((*var.vel)[i][j]) > small_vel) {
+
+                        double f_C = param.control.damping_factor * std::copysign(force[i][j], (*var.vel)[i][j]);
+                        double f_V = critical_coeff * (*var.vel)[i][j];
+                        double f_damping = (std::fabs(f_C) < std::fabs(f_V)) ? f_V : f_C;
+                        force[i][j] -= f_damping;
+                    }
+            }
+        }
         break;
     default:
         std::cerr << "Error: unknown damping_option: " << param.control.damping_option << '\n';
@@ -572,7 +614,6 @@ void update_force(const Param& param, const Variables& var, array_t& force, arra
     if (!param.ic.has_body_force_adjustment) apply_stress_bcs_neumann(param, var, force);
     apply_damping(param, var, force);
     
-
 #ifdef USE_NPROF
     nvtxRangePop();
 #endif
@@ -600,7 +641,6 @@ double calculate_residual_force(const Variables& var, array_t& force_residual)
 }
 
 
-
 void update_velocity(const Variables& var, array_t& vel)
 {
 #ifdef USE_NPROF
diff --git a/geometry.cxx b/geometry.cxx
index 940dc1f1..d5d599ed 100644
--- a/geometry.cxx
+++ b/geometry.cxx
@@ -344,7 +344,8 @@ void NMD_stress(const Param& param, const Variables &var,
 #endif
 }
 
-double compute_dt(const Param& param, const Variables& var)
+double compute_dt(const Param& param, Variables& var)
+
 {
 #ifdef USE_NPROF
     nvtxRangePushA(__FUNCTION__);
@@ -357,16 +358,61 @@ double compute_dt(const Param& param, const Variables& var)
     double dt_diffusion = std::numeric_limits<double>::max();
     double dt_hydro_diffusion = std::numeric_limits<double>::max();
     double minl = std::numeric_limits<double>::max();
-    double scaling = 1.0;
+    // Define element velocity arrays
+    double_vec velocity_x_element(var.nelem, 0.0);
+    double_vec velocity_y_element(var.nelem, 0.0);
+    double_vec velocity_z_element(var.nelem, 0.0); // Used only for 3D
+    double vx_element = 0.0, vy_element = 0.0, vz_element = 0.0;
+    // Global max velocity for elements
+    // double global_max_vem = std::max(std::max(var.max_vbc_val, 0.0), 1E-20);
+    double global_max_vem = 0.0;  // 
+    double global_dt_min = std::numeric_limits<double>::max(); // based on length and S wave velocity
+
+    #pragma omp parallel for reduction(min:minl, dt_maxwell, dt_diffusion, dt_hydro_diffusion, global_dt_min) \
+        reduction(max: global_max_vem) \
+        default(none) shared(param, var, velocity_x_element, velocity_y_element, velocity_z_element) \
+        private(vx_element, vy_element, vz_element)
+    #pragma acc parallel loop reduction(min:minl, dt_maxwell, dt_diffusion, dt_hydro_diffusion, global_dt_min) \
+        reduction(max: global_max_vem)
 
-    #pragma omp parallel for reduction(min:minl,dt_maxwell,dt_diffusion,dt_hydro_diffusion)    \
-        default(none) shared(param,var)
-    #pragma acc parallel loop reduction(min:minl, dt_maxwell, dt_diffusion,dt_hydro_diffusion)
     for (int e=0; e<var.nelem; ++e) {
+
+        vx_element = 0.0, vy_element = 0.0, vz_element = 0.0;
         int n0 = (*var.connectivity)[e][0];
         int n1 = (*var.connectivity)[e][1];
         int n2 = (*var.connectivity)[e][2];
 
+        // calculate maxium velocity in the element
+        const array_t& vel = *var.vel;
+        const int *conn = (*var.connectivity)[e];
+        double weight = 1.0 / NODES_PER_ELEM; 
+
+        for (int j = 0; j < NODES_PER_ELEM; ++j) {
+            vx_element += vel[conn[j]][0] * weight;
+            vy_element += vel[conn[j]][1] * weight;
+            #ifdef THREED
+            vz_element += vel[conn[j]][2] * weight;
+            #endif
+        }
+
+        velocity_x_element[e] = vx_element;
+        velocity_y_element[e] = vy_element;
+        #ifdef THREED
+        velocity_z_element[e] = vz_element;
+        #endif
+
+        // min height of this element
+        #ifdef THREED  
+        double max_vem = std::sqrt(vx_element*vx_element + vy_element*vy_element + vz_element*vz_element);
+        #else
+        double max_vem = std::sqrt(vx_element*vx_element + vy_element*vy_element);
+        #endif
+
+        // Find global max velocity
+        global_max_vem = std::max(global_max_vem, max_vem);
+
+        // std::cout<< "Element: " << e << " max_vem: " << global_max_vem << std::scientific << std::setprecision(5) << std::endl;
+
         const double *a = (*var.coord)[n0];
         const double *b = (*var.coord)[n1];
         const double *c = (*var.coord)[n2];
@@ -407,6 +453,9 @@ double compute_dt(const Param& param, const Variables& var)
                                             0.5 * minh * minh / var.mat->hydro_diff_max);
             }
         minl = std::min(minl, minh);
+
+        // Find global min delta t to meet CFL condition
+        global_dt_min = std::min(global_dt_min, minl/std::sqrt(var.mat->shearm(e)/var.mat->rho(e)) /5.0);
     }
 
     double max_vbc_val;
@@ -418,10 +467,28 @@ double compute_dt(const Param& param, const Variables& var)
     else
         max_vbc_val = param.control.characteristic_speed;
 
-    double dt_advection = 0.5 * minl / max_vbc_val;
-    double dt_elastic = (param.control.is_quasi_static) ?
+    double dt_advection = 0;
+    double dt_elastic = 0;
+    global_max_vem = std::max(global_max_vem, var.max_vbc_val);
+    var.max_global_vel_mag = global_max_vem;
+    var.global_dt_min = global_dt_min;
+    
+    // Calculate dt_advection and dt_elastic 
+    if(param.control.has_ATS)
+    {
+        dt_advection = 0.5 * minl / global_max_vem;
+        dt_elastic = (param.control.is_quasi_static) ?
+        0.5 * minl / (global_max_vem * param.control.inertial_scaling) :
+        0.5 * minl / std::sqrt(param.mat.bulk_modulus[0] / param.mat.rho0[0]);
+        dt_elastic = std::max(dt_elastic, global_dt_min); // Ensure dt_elastic is not smaller than global_dt_min
+    }
+    else
+    {
+        dt_advection = 0.5 * minl / max_vbc_val;
+        dt_elastic = (param.control.is_quasi_static) ?
         0.5 * minl / (max_vbc_val * param.control.inertial_scaling) :
         0.5 * minl / std::sqrt(param.mat.bulk_modulus[0] / param.mat.rho0[0]);
+    }
 
     // Combine dt calculations and incorporate dt_hydro_diffusion
     double dt = std::min({dt_elastic, dt_maxwell, dt_advection, dt_diffusion, dt_hydro_diffusion}) * param.control.dt_fraction;
@@ -435,6 +502,7 @@ double compute_dt(const Param& param, const Variables& var)
                   << " " << dt_hydro_diffusion << " " << dt_advection << " " << dt_elastic << "\n";
         std::exit(11);
     }
+    
 #ifdef USE_NPROF
     nvtxRangePop();
 #endif
@@ -504,9 +572,12 @@ double compute_dt_PT(const Param& param, const Variables& var)
         minl = std::min(minl, minh);
     }
 
+
+    // max_vbc_val is maximum boundary velocity
     double max_vbc_val;
     if (param.control.characteristic_speed == 0) {
-        max_vbc_val = var.max_vbc_val;
+        max_vbc_val = var.max_vbc_val; 
+
         if (param.control.surface_process_option > 0)
             max_vbc_val = std::max(max_vbc_val, var.surfinfo.max_surf_vel*5e-1);
     }
@@ -534,7 +605,8 @@ double compute_dt_PT(const Param& param, const Variables& var)
 
 void compute_mass(const Param &param, const Variables &var,
                   double max_vbc_val, double_vec &volume_n,
-                  double_vec &mass, double_vec &tmass, double_vec &hmass, elem_cache &tmp_result)
+                  double_vec &mass, double_vec &tmass, double_vec &hmass, double_vec &ymass, elem_cache &tmp_result)
+
 {
 #ifdef USE_NPROF
     nvtxRangePushA(__FUNCTION__);
@@ -544,7 +616,8 @@ void compute_mass(const Param &param, const Variables &var,
     // mass.assign(mass.size(), 0);
     // tmass.assign(tmass.size(), 0);
 
-    const double pseudo_speed = max_vbc_val * param.control.inertial_scaling;
+    const double pseudo_speed = max_vbc_val * param.control.inertial_scaling; // for non-ATP using max velocity on boundary
+    const double pseudo_speed_ATP = var.max_global_vel_mag * param.control.inertial_scaling; // for ATP using global max velocity
 
     // Retrieve hydraulic properties for the element
     double perm_e = var.mat->perm(0);                // Intrinsic permeability 
@@ -572,23 +645,40 @@ void compute_mass(const Param &param, const Variables &var,
         std::exit(11);
     }
 
-
 #ifdef GPP1X
-    #pragma omp parallel for default(none) shared(var, param, pseudo_speed, tmp_result)
+    #pragma omp parallel for default(none) shared(var, param, pseudo_speed, pseudo_speed_ATP, tmp_result)
 #else
     #pragma omp parallel for default(none) shared(var, param, tmp_result)
 #endif
     #pragma acc parallel loop
     for (int e=0;e<var.nelem;e++) {
         double *tr = tmp_result[e];
-        
-        double rho = (param.control.is_quasi_static) ?
+        double rho;
+
+        if(param.control.has_ATS)
+        {
+            double apprent_speed = std::min(pseudo_speed_ATP, std::sqrt(var.mat->shearm(e)/var.mat->rho(e))); // minimum speed
+
+            rho = (param.control.is_quasi_static) ?
+            (*var.mat).bulkm(e) / (apprent_speed * apprent_speed) :  // pseudo density for quasi-static sim
+            (*var.mat).rho(e);  // true density for dynamic sim
+
+            if (param.control.has_hydraulic_diffusion && (param.control.is_quasi_static == false)) {
+                // Modified density considering porosity for hydraulic diffusion
+                    rho = (*var.mat).rho(e) * (1 - (*var.mat).phi(e)) + 1000.0 * (*var.mat).phi(e);
+                }
+        }
+        else
+        {    
+            rho = (param.control.is_quasi_static) ?
             (*var.mat).bulkm(e) / (pseudo_speed * pseudo_speed) :  // pseudo density for quasi-static sim
-            (*var.mat).rho(e);                                     // true density for dynamic sim
-           
-        if (param.control.has_hydraulic_diffusion && (param.control.is_quasi_static == false)) {
-        // Modified density considering porosity for hydraulic diffusion
-            rho = (*var.mat).rho(e) * (1 - (*var.mat).phi(e)) + 1000.0 * (*var.mat).phi(e);
+            (*var.mat).rho(e);  // true density for dynamic sim
+
+            if (param.control.has_hydraulic_diffusion && (param.control.is_quasi_static == false)) {
+                // Modified density considering porosity for hydraulic diffusion
+                    rho = (*var.mat).rho(e) * (1 - (*var.mat).phi(e)) + 1000.0 * (*var.mat).phi(e);
+                }
+
         }
 
         double bulk_comp = 1.0/(*var.mat).bulkm(e); // lambda + 2G/3
@@ -598,22 +688,27 @@ void compute_mass(const Param &param, const Variables &var,
         double m = rho * (*var.volume)[e] / NODES_PER_ELEM;
         double tm = (*var.mat).rho(e) * (*var.mat).cp(e) * (*var.volume)[e] / NODES_PER_ELEM;
         double hm = (hm_coeff * bulk_comp + (*var.mat).phi(e) * (*var.mat).beta_fluid(e)) * (*var.volume)[e] / NODES_PER_ELEM;
+        double ym = 9 * (*var.mat).bulkm(e) * (*var.mat).shearm(e) / (3 * (*var.mat).bulkm(e) + (*var.mat).shearm(e)) / NODES_PER_ELEM; // Young's modulus
 
         tr[0] = (*var.volume)[e];
         tr[1] = m;
         if (param.control.has_thermal_diffusion)
             tr[2] = tm;
         tr[3] = hm; // check
+        tr[4] = ym; 
     }
 
     #pragma omp parallel for default(none)      \
-        shared(param,var,volume_n,mass,tmass,hmass,tmp_result)
+        shared(param,var,volume_n,mass,tmass,hmass,ymass,tmp_result)
+
     #pragma acc parallel loop
     for (int n=0;n<var.nnode;n++) {
         volume_n[n]=0;
         mass[n]=0;
         tmass[n]=0;
         hmass[n]=0;
+        ymass[n]=0;
+      
         for( auto e = (*var.support)[n].begin(); e < (*var.support)[n].end(); ++e) {
             double *tr = tmp_result[*e];
             volume_n[n] += tr[0];
@@ -621,6 +716,7 @@ void compute_mass(const Param &param, const Variables &var,
             if (param.control.has_thermal_diffusion)
                 tmass[n] += tr[2];
             hmass[n] += tr[3];
+            ymass[n] += tr[4];
         }
     }
 
@@ -629,7 +725,6 @@ void compute_mass(const Param &param, const Variables &var,
 #endif
 }
 
-
 void compute_shape_fn(const Variables &var, shapefn &shpdx, shapefn &shpdy, shapefn &shpdz)
 {
 #ifdef USE_NPROF
diff --git a/geometry.hpp b/geometry.hpp
index 7467baf8..d9bce785 100644
--- a/geometry.hpp
+++ b/geometry.hpp
@@ -16,13 +16,22 @@ void compute_edvoldt(const Variables &var, double_vec &dvoldt,
 void NMD_stress(const Param& param, const Variables &var, double_vec &dp_nd,
                 tensor_t& stress, double_vec &tmp_result_sg);
 
-double compute_dt(const Param& param, const Variables& var);
+double compute_dt(const Param& param, Variables& var);
+// double compute_dt(const Param& param, const Variables& var);
+
+double compute_dt_PT(const Param& param, const Variables& var);
+
+// void compute_mass(const Param &param, const Variables &var,
+//                   double max_vbc_val, double_vec &volume_n,
+//                   double_vec &mass, double_vec &tmass, double_vec &hmass,
+//                   elem_cache &tmp_result);
 
 double compute_dt_PT(const Param& param, const Variables& var);
 
 void compute_mass(const Param &param, const Variables &var,
                   double max_vbc_val, double_vec &volume_n,
-                  double_vec &mass, double_vec &tmass, double_vec &hmass,
+                  double_vec &mass, double_vec &tmass, double_vec &hmass, double_vec &ymass,
+
                   elem_cache &tmp_result);
 
 void compute_shape_fn(const Variables &var, shapefn &shpdx,
diff --git a/ic.hpp b/ic.hpp
index 73963c61..4e9aaf25 100644
--- a/ic.hpp
+++ b/ic.hpp
@@ -13,6 +13,7 @@ void initial_weak_zone(const Param &param, const Variables &var,
                        double_vec &plstrain);
 void initial_temperature(const Param &param, const Variables &var,
                          double_vec &temperature, double_vec &radiogenic_source, double &bottom_temperature);
+
 void initial_hydrostatic_state(const Param &param, const Variables &var,
                           double_vec &ppressure, double_vec &dppressure);
 
diff --git a/input.cxx b/input.cxx
index 5ba920ac..2a550ce1 100644
--- a/input.cxx
+++ b/input.cxx
@@ -282,6 +282,8 @@ static void declare_parameters(po::options_description &cfg,
 
         ("control.has_PT", po::value<bool>(&p.control.has_PT)->default_value(false),
          "Does the model have Pseudo-transient (PT) loop?\n")
+        ("control.PT_jump", po::value<bool>(&p.control.PT_jump)->default_value(false),
+         "Skip certain processes in PT loop to avoid accumulative effects. For example, surface diffusion.\n")
         ("control.PT_max_iter", po::value<int>(&p.control.PT_max_iter)->default_value(5000),
          "Maximum iteration for PT loop")
         ("control.PT_relative_tolerance",po::value<double>(&p.control.PT_relative_tolerance)->default_value(1e-6),
@@ -289,6 +291,8 @@ static void declare_parameters(po::options_description &cfg,
 
          ("control.has_moving_mesh", po::value<bool>(&p.control.has_moving_mesh)->default_value(true),
          "Does the model update mesh coordinates (Lagrangian)?\n")
+         ("control.has_ATS", po::value<bool>(&p.control.has_ATS)->default_value(false),
+         "Does the model consider adaptive time stepping?\n")
         ;
 
     cfg.add_options()
@@ -570,7 +574,7 @@ static void declare_parameters(po::options_description &cfg,
     cfg.add_options()
         ("mat.rheology_type", po::value<std::string>()->required(),
          "Type of rheology, either 'elastic', 'viscous' (experimental), 'maxwell', "
-         "'elasto-plastic', or 'elasto-visco-plastic'.")
+         "'elasto-plastic', 'elasto-visco-plastic', 'elasto-plastic-rsf', or 'elasto-visco-plastic-rsf'")
         ("mat.is_plane_strain", po::value<bool>(&p.mat.is_plane_strain)->default_value(false),
          "Is the rheology formulation in plane strain (2D elasto-plastic case only)?\n")
 
@@ -666,6 +670,13 @@ static void declare_parameters(po::options_description &cfg,
          "Biot-Willis coefficient (HM coupling coeff) '[d0, d1, d2, ...]' (no unit)")
         ("mat.bulk_modulus_s", po::value<std::string>()->default_value("[37e9]"),
          "Bulk modulus of the solid grains or minerals '[d0, d1, d2, ...]' (Pa)")
+        // for rate-and-state friction
+        ("mat.direct_a", po::value<std::string>()->default_value("[0.020]"),
+         "rate-and-state friction parameter a '[d0, d1, d2, ...]' (-)")
+        ("mat.evolution_b", po::value<std::string>()->default_value("[0.025]"),
+         "rate-and-state friction parameter b '[d0, d1, d2, ...]' (-)")
+        ("mat.characteristic_velocity", po::value<std::string>()->default_value("[1e-6]"),
+         "rate-and-state friction parameter V0 '[d0, d1, d2, ...]' (-)")
         ;
     /*
     Example of bulk modulus of rock-forming minerals
@@ -920,8 +931,8 @@ static void validate_parameters(const po::variables_map &vm, Param &p)
             std::cerr << "Error: bc.vbc_z0 is not 0, 1, 2, or 3.\n";
             std::exit(1);
         }
-        if ( p.bc.vbc_z1 > 3) {
-            std::cerr << "Error: bc.vbc_z0 is not 0, 1, 2, or 3.\n";
+        if ( p.bc.vbc_z1 > 4) {
+            std::cerr << "Error: bc.vbc_z0 is not 0, 1, 2, 3, or 4.\n";
             std::exit(1);
         }
         if ( p.bc.vbc_n0 != 1 && p.bc.vbc_n0 != 3 && p.bc.vbc_n0 != 11 && p.bc.vbc_n0 != 13 ) {
@@ -995,6 +1006,10 @@ static void validate_parameters(const po::variables_map &vm, Param &p)
             p.mat.rheol_type = MatProps::rh_ep;
         else if (str == std::string("elasto-visco-plastic"))
             p.mat.rheol_type = MatProps::rh_evp;
+        else if (str == std::string("elasto-plastic-rsf"))
+            p.mat.rheol_type = MatProps::rh_ep_rsf;
+        else if (str == std::string("elasto-visco-plastic-rsf"))
+            p.mat.rheol_type = MatProps::rh_evp_rsf;
         else {
             std::cerr << "Error: unknown rheology: '" << str << "'\n";
             std::exit(1);
@@ -1076,6 +1091,10 @@ static void validate_parameters(const po::variables_map &vm, Param &p)
         get_numbers(vm, "mat.fluid_visc", p.mat.fluid_visc, p.mat.nmat, 1);
         get_numbers(vm, "mat.biot_coeff", p.mat.biot_coeff, p.mat.nmat, 1);
         get_numbers(vm, "mat.bulk_modulus_s", p.mat.bulk_modulus_s, p.mat.nmat, 1);
+        // Rate-and-state friction parameters
+        get_numbers(vm, "mat.direct_a", p.mat.direct_a, p.mat.nmat, 1);
+        get_numbers(vm, "mat.evolution_b", p.mat.evolution_b, p.mat.nmat, 1);
+        get_numbers(vm, "mat.characteristic_velocity", p.mat.characteristic_velocity, p.mat.nmat, 1);
     }
 
 }
diff --git a/matprops.cxx b/matprops.cxx
index 0983f069..57cc539a 100644
--- a/matprops.cxx
+++ b/matprops.cxx
@@ -202,6 +202,12 @@ MatProps::MatProps(const Param& p, const Variables& var) :
     fluid_visc = p.mat.fluid_visc;
     biot_coeff = p.mat.biot_coeff;
     bulk_modulus_s = p.mat.bulk_modulus_s;
+    // Rate-and-state friction parameters
+    direct_a = p.mat.direct_a;
+    evolution_b = p.mat.evolution_b;
+    characteristic_velocity = p.mat.characteristic_velocity;
+    // static_friction_coefficient = p.mat.static_friction_coefficient;
+
 }
 
 
@@ -211,6 +217,7 @@ MatProps::~MatProps()
     #pragma acc exit data delete(visc_coefficient,visc_activation_energy,heat_capacity,therm_cond,pls0)
     #pragma acc exit data delete(pls1,cohesion0,friction_angle0,friction_angle1,dilation_angle0,dilation_angle1)
     #pragma acc exit data delete(porosity,hydraulic_perm,fluid_rho0,fluid_alpha,fluid_bulk_modulus,fluid_visc,biot_coeff,bulk_modulus_s)
+    #pragma acc exit data delete(direct_a,evolution_b,characteristic_velocity)
 }
 
 
@@ -305,6 +312,64 @@ void MatProps::plastic_weakening(int e, double pls,
     hardening = h / n;
 }
 
+void MatProps::plastic_weakening_rsf(int e, double pls,
+                                 double &cohesion, double &friction_angle,
+                                 double &dilation_angle, double &hardening, double &slip_rate) const
+{
+    double c, f, d, h;
+    c = f = d = h = 0;
+
+    double d_a, e_b, c_v, mu_0, mu_d;
+    d_a = e_b = c_v = mu_0 = mu_d = 0;
+
+    double static_friction_angle = 0;
+
+    double 
+    int n = 0;
+    for (int m=0; m<nmat; m++) {
+        int k = elemmarkers[e][m];
+        if (k == 0) continue;
+        n += k;
+        if (pls < pls0[m]) {
+            // no weakening yet
+            c += cohesion0[m] * k;
+            f += friction_angle0[m] * k;
+            d += dilation_angle0[m] * k;
+            h += 0;
+        }
+        else if (pls < pls1[m]) {
+            // linear weakening
+            double p = (pls - pls0[m]) / (pls1[m] - pls0[m]);
+            c += (cohesion0[m] + p * (cohesion1[m] - cohesion0[m])) * k;
+            f += (friction_angle0[m] + p * (friction_angle1[m] - friction_angle0[m])) * k;
+            d += (dilation_angle0[m] + p * (dilation_angle1[m] - dilation_angle0[m])) * k;
+            h += (cohesion1[m] - cohesion0[m]) / (pls1[m] - pls0[m]) * k;
+        }
+        else {
+            // saturated weakening
+            c += cohesion1[m] * k;
+            f += friction_angle1[m] * k;
+            d += dilation_angle1[m] * k;
+            h += 0;
+        }
+
+        // Rate-and-state friction parameters
+        d_a += direct_a[m] * k;
+        e_b += evolution_b[m] * k;
+        c_v += characteristic_velocity[m] * k;
+    }
+
+    d_a /= n; // direct effect parameter
+    e_b /= n; // evolution effect parameter   
+    c_v /= n; // characteristic velocity
+    static_friction_angle = f / n; // friction angle
+    mu_0 = std::tan(DEG2RAD * static_friction_angle); // static friction coefficient
+    mu_d = mu_0 + (d_a - e_b) * log(slip_rate / c_v); // dynamic friction angle
+    friction_angle = std::atan(mu_d) / DEG2RAD;
+    cohesion = c / n;
+    dilation_angle = d / n;
+    hardening = h / n;
+}
 
 void MatProps::plastic_props(int e, double pls,
                              double& amc, double& anphi, double& anpsi,
@@ -325,6 +390,25 @@ void MatProps::plastic_props(int e, double pls,
     ten_max = (phi == 0)? tension_max : std::min(tension_max, cohesion/std::tan(phi*DEG2RAD));
 }
 
+void MatProps::plastic_props_rsf(int e, double pls,
+                             double& amc, double& anphi, double& anpsi,
+                             double& hardn, double& ten_max, double& slip_rate) const
+{
+    double cohesion, phi, psi;
+
+    plastic_weakening_rsf(e, pls, cohesion, phi, psi, hardn, slip_rate);
+
+    // derived variables
+    double sphi = std::sin(phi * DEG2RAD);
+    double spsi = std::sin(psi * DEG2RAD);
+
+    anphi = (1 + sphi) / (1 - sphi);
+    anpsi = (1 + spsi) / (1 - spsi);
+    amc = 2 * cohesion * std::sqrt(anphi);
+
+    ten_max = (phi == 0)? tension_max : std::min(tension_max, cohesion/std::tan(phi*DEG2RAD));
+}
+
 
 double MatProps::rho(int e) const
 {
@@ -436,6 +520,23 @@ double MatProps::beta_mineral(int e) const
     return 1.0 / harmonic_mean(bulk_modulus_s, elemmarkers[e]);
 }
 
+// Rate-and-state friction parameters
+double MatProps::d_a(int e) const
+{
+    return arithmetic_mean(direct_a, elemmarkers[e]);
+}
+
+double MatProps::e_b(int e) const
+{
+    return arithmetic_mean(evolution_b, elemmarkers[e]);
+}
+
+double MatProps::c_v(int e) const
+{
+    return arithmetic_mean(characteristic_velocity, elemmarkers[e]);
+}
+
+
 #else
 
 namespace {
@@ -687,6 +788,11 @@ MatProps::MatProps(const Param& p, const Variables& var) :
     fluid_visc = VectorBase::create(p.mat.fluid_visc, nmat);
     biot_coeff = VectorBase::create(p.mat.biot_coeff, nmat);
     bulk_modulus_s = VectorBase::create(p.mat.bulk_modulus_s, nmat);
+    // Rate-and-state friction parameters
+    direct_a = VectorBase::create(p.mat.direct_a, nmat);
+    evolution_b = VectorBase::create(p.mat.evolution_b, nmat);
+    characteristic_velocity = VectorBase::create(p.mat.characteristic_velocity, nmat);
+    // static_friction_coefficient = VectorBase::create(p.mat.static_friction_coefficient, nmat);
 }
 
 
@@ -720,6 +826,11 @@ MatProps::~MatProps()
     delete fluid_visc;
     delete biot_coeff;
     delete bulk_modulus_s;
+    // Deleting rate-and-state friction properties
+    delete direct_a;
+    delete evolution_b;
+    delete characteristic_velocity;
+    // delete static_friction_coefficient;
 }
 
 
@@ -819,6 +930,72 @@ void MatProps::plastic_weakening(int e, double pls,
     hardening = h / n;
 }
 
+void MatProps::plastic_weakening_rsf(int e, double pls,
+                                 double &cohesion, double &friction_angle,
+                                 double &dilation_angle, double &hardening, double &slip_rate) const
+{
+    double c, f, d, h;
+    c = f = d = h = 0;
+
+    double d_a, e_b, c_v, mu_0, mu_d;
+    d_a = e_b = c_v = mu_0 = mu_d = 0;
+
+    double static_friction_angle = 0;
+
+    int n = 0;
+    for (int m=0; m<nmat; m++) {
+        int k = elemmarkers[e][m];
+        if (k == 0) continue;
+        n += k;
+        if (pls < (*pls0)[m]) {
+            // no weakening yet
+            c += (*cohesion0)[m] * k;
+            f += (*friction_angle0)[m] * k;
+            d += (*dilation_angle0)[m] * k;
+            h += 0;
+        }
+        else if (pls < (*pls1)[m]) {
+            // linear weakening
+            double p = (pls - (*pls0)[m]) / ((*pls1)[m] - (*pls0)[m]);
+            c += ((*cohesion0)[m] + p * ((*cohesion1)[m] - (*cohesion0)[m])) * k;
+            f += ((*friction_angle0)[m] + p * ((*friction_angle1)[m] - (*friction_angle0)[m])) * k;
+            d += ((*dilation_angle0)[m] + p * ((*dilation_angle1)[m] -(* dilation_angle0)[m])) * k;
+            h += ((*cohesion1)[m] - (*cohesion0)[m]) / ((*pls1)[m] - (*pls0)[m]) * k;
+        }
+        else {
+            // saturated weakening
+            c += (*cohesion1)[m] * k;
+            f += (*friction_angle1)[m] * k;
+            d += (*dilation_angle1)[m] * k;
+            h += 0;
+        }
+
+        // Rate-and-state friction parameters
+        d_a += (*direct_a)[m] * k;
+        e_b += (*evolution_b)[m] * k;
+        c_v += (*characteristic_velocity)[m] * k;
+    }
+
+    d_a /= n; // direct effect parameter
+    e_b /= n; // evolution effect parameter   
+    c_v /= n; // characteristic velocity
+    static_friction_angle = f / n; // friction angle
+    mu_0 = std::tan(DEG2RAD * static_friction_angle); // static friction coefficient
+    mu_d = mu_0 + (d_a - e_b) * log(slip_rate / c_v); // dynamic friction angle
+    friction_angle = std::atan(mu_d) / DEG2RAD;
+    cohesion = c / n;
+    dilation_angle = d / n;
+    hardening = h / n;
+
+    // std::cout << "d_a: " << d_a << " e_b: " << e_b << " c_v: " << c_v << " mu_0: " << mu_0 << " mu_d: " << mu_d << " friction_angle: " << friction_angle << std::endl;
+
+    // std::cout << "d_a: " << d_a << std::endl;
+    // std::cout << "e_b: " << e_b << std::endl;  
+    // std::cout << "c_v: " << c_v << std::endl;
+    // std::cout << "mu_0: " << mu_0 << std::endl;
+    // std::cout << "mu_d: " << mu_d << std::endl;
+    // std::cout << "friction_angle: " << friction_angle << std::endl;
+}
 
 void MatProps::plastic_props(int e, double pls,
                              double& amc, double& anphi, double& anpsi,
@@ -839,6 +1016,24 @@ void MatProps::plastic_props(int e, double pls,
     ten_max = (phi == 0)? tension_max : std::min(tension_max, cohesion/std::tan(phi*DEG2RAD));
 }
 
+void MatProps::plastic_props_rsf(int e, double pls,
+                             double& amc, double& anphi, double& anpsi,
+                             double& hardn, double& ten_max, double& slip_rate) const
+{
+    double cohesion, phi, psi;
+
+    plastic_weakening_rsf(e, pls, cohesion, phi, psi, hardn, slip_rate);
+
+    // derived variables
+    double sphi = std::sin(phi * DEG2RAD);
+    double spsi = std::sin(psi * DEG2RAD);
+
+    anphi = (1 + sphi) / (1 - sphi);
+    anpsi = (1 + spsi) / (1 - spsi);
+    amc = 2 * cohesion * std::sqrt(anphi);
+
+    ten_max = (phi == 0)? tension_max : std::min(tension_max, cohesion/std::tan(phi*DEG2RAD));
+}
 
 double MatProps::rho(int e) const
 {
@@ -950,4 +1145,20 @@ double MatProps::beta_mineral(int e) const
     return 1.0 / harmonic_mean(*bulk_modulus_s, elemmarkers[e]);
 }
 
+// Rate-and-state friction parameters
+double MatProps::d_a(int e) const
+{
+    return arithmetic_mean(*direct_a, elemmarkers[e]);
+}
+
+double MatProps::e_b(int e) const
+{
+    return arithmetic_mean(*evolution_b, elemmarkers[e]);
+}
+
+double MatProps::c_v(int e) const
+{
+    return arithmetic_mean(*characteristic_velocity, elemmarkers[e]);
+}
+
 #endif
\ No newline at end of file
diff --git a/matprops.hpp b/matprops.hpp
index 5f294bf0..94ae9f4a 100644
--- a/matprops.hpp
+++ b/matprops.hpp
@@ -51,11 +51,23 @@ class MatProps
     double alpha_biot(int e) const;
     #pragma acc routine seq
     double beta_mineral(int e) const;
-    
+
+    // rate-and-state friction parameters
+    #pragma acc routine seq
+    double d_a(int e) const;
+    #pragma acc routine seq
+    double e_b(int e) const;
+    #pragma acc routine seq
+    double c_v(int e) const;
+
     #pragma acc routine seq
     void plastic_props(int e, double pls,
                        double& amc, double& anphi, double& anpsi,
                        double& hardn, double& ten_max) const;
+    #pragma acc routine seq
+    void plastic_props_rsf(int e, double pls,
+                       double& amc, double& anphi, double& anpsi,
+                       double& hardn, double& ten_max, double& slip_rate) const;
 
     const bool is_plane_strain;
     const double visc_min;
@@ -64,13 +76,16 @@ class MatProps
     const double therm_diff_max;
     double hydro_diff_max;
 
-    const static int rh_elastic = 1 << 0;
-    const static int rh_viscous = 1 << 1;
-    const static int rh_plastic = 1 << 2;
-    const static int rh_plastic2d = rh_plastic | 1 << 3;
-    const static int rh_maxwell = rh_elastic | rh_viscous;
-    const static int rh_ep = rh_elastic | rh_plastic;
-    const static int rh_evp = rh_elastic | rh_viscous | rh_plastic;
+    const static int rh_elastic = 1 << 0; // Decimal value 1
+    const static int rh_viscous = 1 << 1; // Decimal value 2
+    const static int rh_plastic = 1 << 2; // Decimal value 4
+    const static int rh_plastic2d = rh_plastic | 1 << 3; // Decimal value 12
+    const static int rh_rsf = 1 << 4; // Decimal value 16
+    const static int rh_maxwell = rh_elastic | rh_viscous; // Decimal value 3
+    const static int rh_ep = rh_elastic | rh_plastic; // Decimal value 5
+    const static int rh_evp = rh_elastic | rh_viscous | rh_plastic; // Decimal value 7
+    const static int rh_ep_rsf = rh_elastic | rh_plastic | rh_rsf;  // Decimal value 21
+    const static int rh_evp_rsf = rh_elastic | rh_viscous | rh_plastic | rh_rsf; // Decimal value 23
 
 private:
 
@@ -102,10 +117,19 @@ class MatProps
     VectorBase fluid_alpha, fluid_bulk_modulus, fluid_visc;
     VectorBase biot_coeff, bulk_modulus_s;
 
+    // rate-and-state friction
+    VectorBase direct_a, evolution_b, characteristic_velocity;
+    // VectorBase static_friction_coefficient;
+
     #pragma acc routine seq
     void plastic_weakening(int e, double pls,
                            double &cohesion, double &friction_angle,
-                           double &dilation_angle, double &hardening) const;
+                           double &dilation_angle, double &hardening, double& slip_rate) const;
+
+    #pragma acc routine seq
+    void plastic_weakening_rsf(int e, double pls,
+                           double &cohesion, double &friction_angle,
+                           double &dilation_angle, double &hardening, double &slip_rate) const;
 };
 
 #else
@@ -150,9 +174,17 @@ class MatProps
     double alpha_biot(int e) const;
     double beta_mineral(int e) const;
 
+    // rate-and-state friction parameters
+    double d_a(int e) const;
+    double e_b(int e) const;
+    double c_v(int e) const;
+
     void plastic_props(int e, double pls,
                        double& amc, double& anphi, double& anpsi,
                        double& hardn, double& ten_max) const;
+    void plastic_props_rsf(int e, double pls,
+                       double& amc, double& anphi, double& anpsi,
+                       double& hardn, double& ten_max, double& slip_rate) const;
 
     const bool is_plane_strain;
     const double visc_min;
@@ -165,9 +197,12 @@ class MatProps
     const static int rh_viscous = 1 << 1;
     const static int rh_plastic = 1 << 2;
     const static int rh_plastic2d = rh_plastic | 1 << 3;
+    const static int rh_rsf = 1 << 4; // Decimal value 16
     const static int rh_maxwell = rh_elastic | rh_viscous;
     const static int rh_ep = rh_elastic | rh_plastic;
     const static int rh_evp = rh_elastic | rh_viscous | rh_plastic;
+    const static int rh_ep_rsf = rh_elastic | rh_plastic | rh_rsf;  // Decimal value 21
+    const static int rh_evp_rsf = rh_elastic | rh_viscous | rh_plastic | rh_rsf; // Decimal value 23
 
 private:
 
@@ -198,9 +233,17 @@ class MatProps
     const VectorBase *fluid_alpha, *fluid_bulk_modulus, *fluid_visc;
     const VectorBase *biot_coeff, *bulk_modulus_s;
 
+    // rate-and-state friction
+    const VectorBase *direct_a, *evolution_b, *characteristic_velocity;
+    // const VectorBase *static_friction_coefficient;   
+
     void plastic_weakening(int e, double pls,
                            double &cohesion, double &friction_angle,
                            double &dilation_angle, double &hardening) const;
+
+    void plastic_weakening_rsf(int e, double pls,
+                           double &cohesion, double &friction_angle,
+                           double &dilation_angle, double &hardening, double &slip_rate) const;
 };
 
 
diff --git a/output.cxx b/output.cxx
index 877b4b24..278d69d1 100644
--- a/output.cxx
+++ b/output.cxx
@@ -195,6 +195,7 @@ void Output::_write(const Variables& var, bool disable_averaging)
         std::cout << "  Output # " << frame
               << ", step = " << var.steps
               << ", time = " << std::scientific << std::setprecision(5) << var.time / YEAR2SEC << " yr"
+              << ", vmax = " << var.max_global_vel_mag << " m/s"
               << ", dt = " << std::scientific << std::setprecision(5) << dt / YEAR2SEC << " yr"
               << ", wt = ";
         print_time_ns(duration_ns);
@@ -205,12 +206,12 @@ void Output::_write(const Variables& var, bool disable_averaging)
         std::cout << "  Output # " << frame
               << ", step = " << var.steps
               << ", time = " << std::scientific << std::setprecision(5) << var.time << " sec"
+              << ", vmax = " << var.max_global_vel_mag << " m/s"
               << ", dt = " << std::scientific << std::setprecision(5) << dt<< " sec"
               << ", wt = ";
         print_time_ns(duration_ns);
         std::cout << "\n";
     }
-    
 
     frame ++;
 
diff --git a/parameters.hpp b/parameters.hpp
index 56df5f37..1c8c8514 100644
--- a/parameters.hpp
+++ b/parameters.hpp
@@ -126,10 +126,13 @@ struct Control {
     double hydration_migration_speed;
 
     bool has_PT;
+    mutable bool PT_jump;
     int PT_max_iter;
     double PT_relative_tolerance;
 
     bool has_moving_mesh;
+    bool has_ATS;
+
 };
 
 struct BC {
@@ -309,6 +312,13 @@ struct Mat {
     double_vec fluid_visc;  // pore fluid dynamic viscosity
     double_vec biot_coeff;  // Biot-Willis coefficient
     double_vec bulk_modulus_s;  // bulk modulus of solid grain (mineral)
+  
+    // rate-and-state friction parameters
+    double_vec direct_a;
+    double_vec evolution_b;
+    double_vec characteristic_velocity;
+    // double_vec static_friction_coefficient;
+
 };
 
 struct Time {
@@ -421,9 +431,21 @@ struct Variables {
     int nx, ny, nz, ncell;
 
     double max_vbc_val;
+    double max_global_vel_mag;
+    double global_dt_min;
     double compensation_pressure;
     double bottom_temperature;
 
+// #ifdef ATS
+//     double vmax, hmin, dt_elastic, dt_min, vmax_shear_zone, CL_min;
+// #endif    
+//     double a0, b0, a1, b1, max_pls, number_plf, seismic_eff, S_E, K_E;
+
+//     double_vec *MAX_shear, *CL, *dl_min, *maxv, *strain_energy, *kinetic_energy, *MAX_shear_0;
+// #ifdef RS
+//     double_vec *state1, *slip_velocity, *friction_coefficient, *RS_shear, *Failure_mode, avg_shear_stress, avg_vm, slip_area;
+// #endif
+
     // These 5 arrays are allocated by external library
     array_t *coord;
     conn_t *connectivity;
@@ -460,6 +482,7 @@ struct Variables {
     double_vec *volume, *volume_old, *volume_n;
     double_vec *mass, *tmass;
     double_vec *hmass;
+    double_vec *ymass; // Young's modulus for nodes
     double_vec *edvoldt;
     double_vec *temperature, *plstrain, *delta_plstrain;
     double_vec *stressyy, *dpressure, *viscosity;
diff --git a/remeshing.cxx b/remeshing.cxx
index 9f9c76c4..977cfaac 100644
--- a/remeshing.cxx
+++ b/remeshing.cxx
@@ -3157,7 +3157,11 @@ void remesh(const Param &param, Variables &var, int bad_quality)
     compute_volume(*var.coord, *var.connectivity, *var.volume);
     // TODO: using edvoldt and volume to get volume_old
     std::copy(var.volume->begin(), var.volume->end(), var.volume_old->begin());
-    compute_mass(param, var, var.max_vbc_val, *var.volume_n, *var.mass, *var.tmass, *var.hmass, *var.tmp_result);
+
+    if(param.control.has_ATS)
+        var.dt = compute_dt(param, var);
+    compute_mass(param, var, var.max_vbc_val, *var.volume_n, *var.mass, *var.tmass, *var.hmass, *var.ymass, *var.tmp_result);
+
     compute_shape_fn(var, *var.shpdx, *var.shpdy, *var.shpdz);
 
     if (param.mesh.remeshing_option==1 ||
diff --git a/rheology.cxx b/rheology.cxx
index 229b08bc..08c5ad17 100644
--- a/rheology.cxx
+++ b/rheology.cxx
@@ -18,6 +18,7 @@ static void principal_stresses3(const double* s, double p[3], double v[3][3])
     /* s is a flattened stress vector, with the components {XX, YY, ZZ, XY, XZ, YZ}.
      * Returns the eigenvalues p and eignvectors v.
      * The eigenvalues are ordered such that p[0] <= p[1] <= p[2].
+     *  - Maximum shear stress direction 'shear_dir'.
      */
 
     // unflatten s to a 3x3 tensor, only the upper part is needed.
@@ -104,6 +105,95 @@ static void principal_stresses2(const double* s, double p[2],
     }
 }
 
+#pragma acc routine seq
+static void compute_slip_rate2(const double* s, double& vx, double& vz, double& slip_rate)
+{
+    /* Computes the slip magnitude by projecting velocity (vx, vz) onto the maximum shear direction.
+     *
+     * Inputs:
+     *  - s: Flattened stress tensor {σ_xx, σ_zz, σ_xz}
+     *  - vx, vz: Velocity components
+     * 
+     * Output:
+     *  - slip: The scalar magnitude of the slip vector (computed by projection)
+     */
+
+    // Compute center and radius of Mohr circle
+    double s0 = 0.5 * (s[0] + s[1]);
+    double rad = second_invariant(s);
+    double cos2t, sin2t;
+
+    {
+        // Compute direction cosine and sine of 2*theta
+        const double eps = 1e-15;
+        double a = 0.5 * (s[0] - s[1]);
+        double b = -rad; // Always negative
+        if (b < -eps) {
+            cos2t = a / b;
+            sin2t = s[2] / b;
+        }
+        else {
+            cos2t = 1;
+            sin2t = 0;
+        }
+    }
+
+    // Compute shear direction (rotated 45° from principal stress direction)
+    double theta_shear = 0.5 * atan2(sin2t, cos2t);
+    double shear_dir[2];
+    shear_dir[0] = cos(theta_shear + M_PI_4); // Shear direction X-component
+    shear_dir[1] = sin(theta_shear + M_PI_4); // Shear direction Z-component
+
+    // slip magnitude
+    slip_rate = std::abs(vx * shear_dir[0] + vz * shear_dir[1]);
+}
+
+#pragma acc routine seq
+static void compute_slip_rate3(const double* s, double vx, double vy, double vz, double& slip_rate)
+{
+    /* Computes the slip magnitude by projecting velocity (vx, vy, vz) onto the maximum shear direction in 3D.
+     *
+     * Inputs:
+     *  - s: Flattened 3D stress tensor {σ_xx, σ_yy, σ_zz, σ_xy, σ_xz, σ_yz}
+     *  - vx, vy, vz: Velocity components
+     * 
+     * Output:
+     *  - slip_rate: Scalar magnitude of the slip vector (computed by projection)
+     */
+
+    double p[3], v[3][3];
+
+    // Compute principal stresses and eigenvectors
+    principal_stresses3(s, p, v);
+
+    // Compute maximum shear stress plane
+    double tau_max_1 = 0.5 * std::abs(p[2] - p[1]);  // Shear between σ3 and σ2
+    double tau_max_2 = 0.5 * std::abs(p[2] - p[0]);  // Shear between σ3 and σ1
+    double tau_max_3 = 0.5 * std::abs(p[1] - p[0]);  // Shear between σ2 and σ1
+
+    int shear_plane_index = (tau_max_2 >= tau_max_1 && tau_max_2 >= tau_max_3) ? 1 :
+                            (tau_max_3 >= tau_max_1 && tau_max_3 >= tau_max_2) ? 2 : 0;
+
+    // Fault normal (perpendicular to max shear stress plane)
+    double fault_normal[3] = {v[0][shear_plane_index], v[1][shear_plane_index], v[2][shear_plane_index]};
+
+    // Two shear directions (perpendicular to fault normal)
+    double shear_dir_1[3] = {v[0][(shear_plane_index + 1) % 3], 
+                             v[1][(shear_plane_index + 1) % 3], 
+                             v[2][(shear_plane_index + 1) % 3]};
+
+    double shear_dir_2[3] = {v[0][(shear_plane_index + 2) % 3], 
+                             v[1][(shear_plane_index + 2) % 3], 
+                             v[2][(shear_plane_index + 2) % 3]};
+
+    // Project velocity onto shear directions
+    double slip_magnitude_1 = vx * shear_dir_1[0] + vy * shear_dir_1[1] + vz * shear_dir_1[2];
+    double slip_magnitude_2 = vx * shear_dir_2[0] + vy * shear_dir_2[1] + vz * shear_dir_2[2];
+
+    // Compute final slip magnitude
+    slip_rate = std::sqrt(slip_magnitude_1 * slip_magnitude_1 + slip_magnitude_2 * slip_magnitude_2);
+}
+
 #pragma acc routine seq
 static void elastic(double bulkm, double shearm, const double* de, double* s)
 {
@@ -541,11 +631,12 @@ static void elasto_plastic2d(double bulkm, double shearm,
     }
 }
 
-void update_stress(const Param& param, const Variables& var, tensor_t& stress,
+void update_stress(const Param& param, Variables& var, tensor_t& stress,
                    double_vec& stressyy, double_vec& dpressure, double_vec& viscosity,
                    tensor_t& strain, double_vec& plstrain,
                    double_vec& delta_plstrain, tensor_t& strain_rate,
-                   double_vec& ppressure, double_vec& dppressure)
+                   double_vec& ppressure, double_vec& dppressure, array_t& vel)
+
 {
 #ifdef USE_NPROF
     nvtxRangePushA(__FUNCTION__);
@@ -554,29 +645,50 @@ void update_stress(const Param& param, const Variables& var, tensor_t& stress,
     // Interpolate pore pressure from nodes to element level
     double_vec ppressure_element(var.nelem, 0.0);
     double_vec dppressure_element(var.nelem, 0.0);
-    #pragma omp parallel for default(none) shared(var, ppressure, ppressure_element, dppressure, dppressure_element)
+    // Define element velocity arrays
+    double_vec velocity_x_element(var.nelem, 0.0);
+    double_vec velocity_y_element(var.nelem, 0.0);
+    double_vec velocity_z_element(var.nelem, 0.0); // Used only for 3D
+
+    #pragma omp parallel for default(none) shared(var, ppressure, ppressure_element, dppressure, dppressure_element, \
+        vel, velocity_x_element, velocity_y_element, velocity_z_element)
     #pragma acc parallel loop
     for (int e = 0; e < var.nelem; e++) {
         const int *conn = (*var.connectivity)[e];
-
         double pp_element = 0.0;
         double dpp_element = 0.0;
+
+        const array_t& vel = *var.vel;
+        double vx_element = 0.0, vy_element = 0.0, vz_element = 0.0;
+
         for (int j = 0; j < NODES_PER_ELEM; ++j) {
         #ifdef THREED
             pp_element += ppressure[conn[j]] / 4.0; // the centroid shape functions are 1/4 for each node in 3D
             dpp_element += dppressure[conn[j]] / 4.0; // the centroid shape functions are 1/4 for each node in 3D
+            vx_element += vel[conn[j]][0] / 4.0; // the centroid shape functions are 1/4 for each node in 3D
+            vy_element += vel[conn[j]][1] / 4.0; // the centroid shape functions are 1/4 for each node in 3D
+            vz_element += vel[conn[j]][2] / 4.0; // the centroid shape functions are 1/4 for each node in 3D
         #else
             pp_element += ppressure[conn[j]] / 3.0; // the centroid shape functions are 1/3 for each node in 2D
-            dpp_element += dppressure[conn[j]] / 3.0; // the centroid shape functions are 1/4 for each node in 3D
+            dpp_element += dppressure[conn[j]] / 3.0; // the centroid shape functions are 1/3 for each node in 2D
+            vx_element += vel[conn[j]][0] / 3.0; // the centroid shape functions are 1/3 for each node in 2D
+            vy_element += vel[conn[j]][1] / 3.0; // the centroid shape functions are 1/3 for each node in 2D
+
         #endif
         }
         ppressure_element[e] = pp_element;  // Store element-level pore pressure 
         dppressure_element[e] = dpp_element;  // Store element-level pore pressure rate
+        velocity_x_element[e] = vx_element;  // Store element-level velocity in x direction
+        velocity_y_element[e] = vy_element;  // Store element-level velocity in y direction
+        #ifdef THREED
+        velocity_z_element[e] = vz_element;  // Store element-level velocity in z direction
+        #endif
     }
 
     #pragma omp parallel for default(none)                           \
         shared(param, var, stress, stressyy, dpressure, viscosity, strain, plstrain, delta_plstrain, \
-               strain_rate, ppressure_element, dppressure_element, std::cerr)
+               strain_rate, ppressure_element, dppressure_element, std::cerr, \
+               velocity_x_element, velocity_y_element, velocity_z_element)
     #pragma acc parallel loop
     for (int e=0; e<var.nelem; ++e) {
         // stress, strain and strain_rate of this element
@@ -591,10 +703,24 @@ void update_stress(const Param& param, const Variables& var, tensor_t& stress,
         double pp = ppressure_element[e];        // Use element-level interpolated pore pressure
         double dpp = dppressure_element[e];        // Use element-level interpolated pore pressure rate
 
-        //  #pragma omp critical
-        // {
-        //     std::cout << e << ", " << ppressure_element[e] << ", " << dppressure_element[e] << ", " << stress[e][2] << std::endl;
+        double vx = velocity_x_element[e];        // Use element-level interpolated velocity in x direction
+        double vy = velocity_y_element[e];        // Use element-level interpolated velocity in y direction
+        double vz = velocity_z_element[e];        // Use element-level interpolated velocity in z direction
+
+        // // Calculate the center of the element
+        // const int *conn = (*var.connectivity)[e];
+        // double center_x = 0., center_z = 0., T = 0.;
+        // for (int i=0; i<NODES_PER_ELEM; ++i){
+        //     center_x += (*var.coord)[conn[i]][0];
+	    // center_z += (*var.coord)[conn[i]][NDIMS-1];
+        //     T += (*var.temperature)[conn[i]];
         // }
+        // T /= NODES_PER_ELEM;
+        // center_x /= NODES_PER_ELEM;
+	    // center_z /= NODES_PER_ELEM;
+        // // Find the most abundant marker mattype in this element
+        // int_vec &a = (*var.elemmarkers)[e];
+        // int material = std::distance(a.begin(), std::max_element(a.begin(), a.end()));
         
         if (param.control.has_hydraulic_diffusion) {
             pp = alpha_b * pp; // Apply Biot coefficient to pore pressure
@@ -699,12 +825,97 @@ void update_stress(const Param& param, const Variables& var, tensor_t& stress,
                 if (var.mat->is_plane_strain) {
                     spyy = syy;
                     elasto_plastic2d(bulkm, shearm, amc, anphi, anpsi, hardn, ten_max,
-                                     de, depls, sp, spyy, failure_mode, 
+                                     de, depls, s, syy, failure_mode, 
+                                     param.control.has_hydraulic_diffusion, dpp);
+                }
+                else {
+                    elasto_plastic(bulkm, shearm, amc, anphi, anpsi, hardn, ten_max,
+                                   de, depls, s, failure_mode, 
+                                   param.control.has_hydraulic_diffusion, dpp);
+                }
+                double spII = second_invariant2(sp);
+
+                // use the smaller as the final stress
+                if (svII < spII)
+                    for (int i=0; i<NSTR; ++i) s[i] = sv[i];
+                else {
+                    for (int i=0; i<NSTR; ++i) s[i] = sp[i];
+                    plstrain[e] += depls;
+                    delta_plstrain[e] = depls;
+                    syy = spyy;
+                }
+            }
+            case MatProps::rh_ep_rsf: // rate-and-state frition model
+            {
+                double depls = 0;
+                double bulkm = var.mat->bulkm(e);
+                double shearm = var.mat->shearm(e);
+
+                // calculate the shear direction in maximum shear stress
+                double slip_rate;
+                
+#ifdef THREED
+                compute_slip_rate3(s, vx, vy, vz, slip_rate); // Calculate slip vector magnitude in 3D
+#else
+                compute_slip_rate2(s, vx, vy, slip_rate); // Calculate slip vector magnitude in 2D
+#endif
+        
+                double amc, anphi, anpsi, hardn, ten_max;
+                var.mat->plastic_props_rsf(e, plstrain[e],
+                                       amc, anphi, anpsi, hardn, ten_max, slip_rate);
+                int failure_mode;
+                if (var.mat->is_plane_strain) {
+                    elasto_plastic2d(bulkm, shearm, amc, anphi, anpsi, hardn, ten_max,
+                                     de, depls, s, syy, failure_mode, 
+                                     param.control.has_hydraulic_diffusion, dpp);
+                }
+                else {
+                    elasto_plastic(bulkm, shearm, amc, anphi, anpsi, hardn, ten_max,
+                                   de, depls, s, failure_mode, 
+                                   param.control.has_hydraulic_diffusion, dpp);
+                }
+                plstrain[e] += depls;
+                delta_plstrain[e] = depls;
+            }
+            break;
+        case MatProps::rh_evp_rsf: // rate-and-state frition model
+            {
+                double depls = 0;
+                double bulkm = var.mat->bulkm(e);
+                double shearm = var.mat->shearm(e);
+                viscosity[e] = var.mat->visc(e);
+                double dv = (*var.volume)[e] / (*var.volume_old)[e] - 1;
+                // stress due to maxwell rheology
+                double sv[NSTR];
+                for (int i=0; i<NSTR; ++i) sv[i] = s[i];
+                maxwell(bulkm, shearm, viscosity[e], var.dt, dv, de, sv);
+                double svII = second_invariant2(sv);
+
+                 // calculate the shear direction in maximum shear stress
+                double slip_rate;
+                
+#ifdef THREED
+                compute_slip_rate3(s, vx, vy, vz, slip_rate); // Calculate slip vector magnitude in 3D
+#else
+                compute_slip_rate2(s, vx, vy, slip_rate); // Calculate slip vector magnitude in 2D
+#endif
+
+                double amc, anphi, anpsi, hardn, ten_max;
+                var.mat->plastic_props_rsf(e, plstrain[e],
+                                       amc, anphi, anpsi, hardn, ten_max, slip_rate);
+                // stress due to elasto-plastic rheology
+                double sp[NSTR], spyy;
+                for (int i=0; i<NSTR; ++i) sp[i] = s[i];
+                int failure_mode;
+                if (var.mat->is_plane_strain) {
+                    spyy = syy;
+                    elasto_plastic2d(bulkm, shearm, amc, anphi, anpsi, hardn, ten_max,
+                                     de, depls, s, syy, failure_mode, 
                                      param.control.has_hydraulic_diffusion, dpp);
                 }
                 else {
                     elasto_plastic(bulkm, shearm, amc, anphi, anpsi, hardn, ten_max,
-                                   de, depls, sp, failure_mode, 
+                                   de, depls, s, failure_mode, 
                                    param.control.has_hydraulic_diffusion, dpp);
                 }
                 double spII = second_invariant2(sp);
@@ -753,45 +964,4 @@ void update_old_mean_stress(const Param& param, const Variables& var, tensor_t&
 #ifdef USE_NPROF
     nvtxRangePop();
 #endif
-}
-
-// void update_stress_old(const Param& param, const Variables& var, tensor_t& stress, tensor_t& stress_old)
-// {
-// #ifdef USE_NPROF
-//     nvtxRangePushA(__FUNCTION__);
-// #endif
-
-//     // Copy current stress into stress_old for each element
-//     #pragma omp parallel for default(none) shared(var, stress, stress_old)
-//     #pragma acc parallel loop
-//     for (int e = 0; e < var.nelem; ++e) {
-//         for (int i = 0; i < NSTR; ++i) {
-//             stress_old[e][i] = stress[e][i];  // Copy current stress to stress_old
-//         }
-//     }
-
-// #ifdef USE_NPROF
-//     nvtxRangePop();
-// #endif
-// }
-
-// void add_stress_old(const Param& param, const Variables& var, tensor_t& stress, tensor_t& stress_old)
-// {
-// #ifdef USE_NPROF
-//     nvtxRangePushA(__FUNCTION__);
-// #endif
-
-//     #pragma omp parallel for default(none) shared(var, stress, stress_old)
-//     #pragma acc parallel loop
-//     for (int e = 0; e < var.nelem; ++e) {
-//         for (int i = 0; i < NSTR; ++i) {
-//             double diff = 0.0;
-//             diff = stress[e][i] - stress_old[e][i];
-//             stress[e][i] += diff;
-//         }
-//     }
-
-// #ifdef USE_NPROF
-//     nvtxRangePop();
-// #endif
-// }
+}
\ No newline at end of file
diff --git a/rheology.hpp b/rheology.hpp
index e3af1901..b7d0ca32 100644
--- a/rheology.hpp
+++ b/rheology.hpp
@@ -1,15 +1,10 @@
 #ifndef DYNEARTHSOL3D_RHEOLOGY_HPP
 #define DYNEARTHSOL3D_RHEOLOGY_HPP
 
-void update_stress(const Param& param ,const Variables& var, tensor_t& stress, double_vec& stressyy,
+void update_stress(const Param& param , Variables& var, tensor_t& stress, double_vec& stressyy,
                    double_vec& dpressure, double_vec& viscosity, tensor_t& strain, double_vec& plstrain,
                    double_vec& delta_plstrain, tensor_t& strain_rate,
-                   double_vec& ppressure, double_vec& dppressure);
+                   double_vec& ppressure, double_vec& dppressure, array_t& vel);
 
 void update_old_mean_stress(const Param& param ,const Variables& var, tensor_t& stress, double_vec& old_mean_stress);
-
-// void update_stress_old(const Param& param ,const Variables& var, tensor_t& stress, tensor_t& stress_old);
-
-// void add_stress_old(const Param& param ,const Variables& var, tensor_t& stress, tensor_t& stress_old);
-
 #endif


Reviews:
user: login: echoi
state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-04-01T14:02:08Z
user: login: echoi
state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-04-01T14:03:57Z
user: login: sungho91
state: COMMENTED
author_association: COLLABORATOR
submitted_at: 2025-04-01T14:50:09Z
user: login: sungho91
state: COMMENTED
author_association: COLLABORATOR
submitted_at: 2025-04-01T14:59:04Z
user: login: copilot-pull-request-reviewer[bot]
body: 

Copilot reviewed 29 out of 46 changed files in this pull request and generated no comments.

<details>
<summary>Files not reviewed (17)</summary>

* **Makefile**: Language not supported
* **benchmarks/1d-consolidation-des2d_traction.cfg**: Language not supported
* **benchmarks/1d-consolidation-des2d_water_loading.cfg**: Language not supported
* **benchmarks/1d-consolidation-des3d_traction.cfg**: Language not supported
* **benchmarks/1d-consolidation-des3d_water_loading.cfg**: Language not supported
* **benchmarks/2d-consolidation.cfg**: Language not supported
* **examples/core-complex.cfg**: Language not supported
* **examples/rate_and_state_friction/stick_slip.poly**: Language not supported
* **examples/rate_and_state_friction/vel_neutral/stick_slip.cfg**: Language not supported
* **examples/rate_and_state_friction/vel_neutral/stick_slip2.cfg**: Language not supported
* **examples/rate_and_state_friction/vel_neutral/stick_slip3.cfg**: Language not supported
* **examples/rate_and_state_friction/vel_strengthening/stick_slip.cfg**: Language not supported
* **examples/rate_and_state_friction/vel_strengthening/stick_slip2.cfg**: Language not supported
* **bc.hpp**: Language not supported
* **Dynearthsol.py**: Language not supported
* **2vtk.py**: Language not supported
* **benchmarks/2d_consolidation-plot.ipynb**: Language not supported
</details>

<details>
<summary>Comments suppressed due to low confidence (3)</summary>

**dynearthsol.cxx:294**
* [nitpick] Consider clarifying or removing the ambiguous comment about Denis's addition to improve code clarity and maintainability.
```
    // For some reason, the following is added by Denis
```
**constants.hpp:37**
* [nitpick] Add a brief comment explaining the purpose of 'nbdrytypes_hydro' to provide context for its introduction.
```
const int nbdrytypes_hydro = 6;
```
**dynearthsol.cxx:271**
* [nitpick] Consider renaming 'ppressure' to 'pore_pressure' for improved readability and consistency.
```
bin_save.read_array(*var.ppressure, "pore pressure");
```
</details>


state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-04-05T15:02:35Z
user: login: echoi
state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-04-05T15:07:45Z
user: login: echoi
state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-04-05T15:13:33Z
user: login: echoi
state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-04-05T15:37:42Z
user: login: echoi
state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-04-05T15:47:35Z
user: login: sungho91
state: COMMENTED
author_association: COLLABORATOR
submitted_at: 2025-04-05T22:11:55Z
user: login: sungho91
state: COMMENTED
author_association: COLLABORATOR
submitted_at: 2025-04-05T22:16:10Z
user: login: sungho91
state: COMMENTED
author_association: COLLABORATOR
submitted_at: 2025-04-05T22:22:16Z
user: login: echoi
state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-04-06T20:18:53Z
user: login: echoi
state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-04-06T20:35:18Z
user: login: sungho91
state: COMMENTED
author_association: COLLABORATOR
submitted_at: 2025-04-07T07:56:33Z
user: login: sungho91
state: COMMENTED
author_association: COLLABORATOR
submitted_at: 2025-04-07T08:00:51Z
user: login: sungho91
state: COMMENTED
author_association: COLLABORATOR
submitted_at: 2025-04-07T08:01:52Z
user: login: echoi
state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-04-09T03:13:44Z
user: login: echoi
state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-04-09T03:14:32Z
user: login: echoi
state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-04-09T03:17:32Z
user: login: echoi
state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-04-09T03:27:12Z
user: login: echoi
state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-04-09T03:46:20Z
user: login: sungho91
state: COMMENTED
author_association: COLLABORATOR
submitted_at: 2025-04-10T11:44:37Z
user: login: chaseshyu
state: COMMENTED
author_association: MEMBER
submitted_at: 2025-04-10T19:02:41Z
user: login: echoi
state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-04-11T16:00:38Z
user: login: tan2
body: Since @echoi has approved the pull request, I will blindly push the merge button. :)
state: APPROVED
author_association: MEMBER
submitted_at: 2025-04-16T03:50:54Z

Review comments:
path: bc.cxx
user: login: echoi
body: Wouldn't it be more convenient to make another case to apply this type of BC? Then, a user can choose the type  in an input file.

created_at: 2025-04-01T14:02:08Z
author_association: CONTRIBUTOR
path: examples/rate_and_state_friction/vel_neutral/stick_slip.cfg
user: login: echoi
body: I suggest renaming the input file names and `modelname` in them such that the key parameters varied among the group of input files can be identified more clearly.
created_at: 2025-04-01T14:03:57Z
author_association: CONTRIBUTOR
path: bc.cxx
user: login: sungho91
body: I don't think it's a good idea to keep adding more hardcoded cases like 4, 5, and so on. We need a more flexible or general approach to setting boundary conditions, similar to what I did in Laghost before. However, implementing this would require some changes to the current DES3D structures. So, it might be better to handle it in a separate commit and pull request later. 
created_at: 2025-04-01T14:50:09Z
author_association: COLLABORATOR
path: examples/rate_and_state_friction/vel_neutral/stick_slip.cfg
user: login: sungho91
body: Good point. However, I think I can fix the model names later, since I'm still working on the examples. There's still room for changes in those examples anyway.
created_at: 2025-04-01T14:59:04Z
author_association: COLLABORATOR
path: bc.cxx
user: login: echoi
body: @sungho91 "Slide block BC" is a trivial modification to the existing cases. And adding cases is much more flexible than requiring users to rebuild the executable to use or not to use a particular functionality. 
created_at: 2025-04-05T15:07:45Z
author_association: CONTRIBUTOR
path: examples/rate_and_state_friction/vel_neutral/stick_slip.cfg
user: login: echoi
body: @sungho91 Your refusal to act on a "good point" is puzzling. 

> I'm still working on the examples

Are you saying the examples are not ready to be merged?
created_at: 2025-04-05T15:13:32Z
author_association: CONTRIBUTOR
path: 2vtk.py
user: login: echoi
body: 1. `output_tensor_components` can be turned on with the command line option `-t`. I don't think it should be on by default.
2. Heat flux outputs are being commented out although it was optional already. I understand that the functionality needs completion and testing in 3D, but wouldn't it be sufficient to set the default value of `output_heatflux` to be `False`?
created_at: 2025-04-05T15:37:42Z
author_association: CONTRIBUTOR
path: bc.cxx
user: login: echoi
body: @sungho91 On a different note, my understanding is that the PT implementation is not complete yet in the sense that it is not showing clear merits. Would you agree? If so, it would be better to exclude the PT-related parts from this pull request.
created_at: 2025-04-05T15:47:35Z
author_association: CONTRIBUTOR
path: bc.cxx
user: login: sungho91
body: @echoi The PT implementation has been completed; however, optimization of the PT loops has not yet begun. If the current inclusion of this unoptimized section is a concern, I'll remove it.
created_at: 2025-04-05T22:11:55Z
author_association: COLLABORATOR
path: examples/rate_and_state_friction/vel_neutral/stick_slip.cfg
user: login: sungho91
body: @echoi I'm a bit confused. What exactly is considered appropriate for merging? Do you think only something that is perfect and fully cleaned up can be merged here?

I believe that you think the current version may confuse users. To find a middle ground, I’ll remove the rate-and-state friction examples, as well as the Makefile and the patch file for bc.cxx, revert to the original version, and push only the core parts for now. Once I finish them, I’ll upload the full version again.
created_at: 2025-04-05T22:16:10Z
author_association: COLLABORATOR
path: 2vtk.py
user: login: sungho91
body: @echoi Thank you for letting me know. I tried to print the pore pressure, but to be honest, I can't clearly remember what exactly I did at that time. Would it be possible to exclude this part from the current pull request?
created_at: 2025-04-05T22:22:15Z
author_association: COLLABORATOR
path: 2vtk.py
user: login: echoi
body: I think you can simply restore the heat flow-related parts and the default value for `output_tensor_components` in `2vtk.py` and commit the file again to this branch.

BTW, `output_heatflux` is set to be `True` when `-heat` command line option is given. It means it's supposed to be `False` by default. So, the change  in line 37 should be retained, I think.

created_at: 2025-04-06T20:18:53Z
author_association: CONTRIBUTOR
path: bc.cxx
user: login: echoi
body: @sungho91 My question is whether the PT algorithm is already providing the merits the reference claims over the current damping and time marching schemes. If it is not or the answer is unknown, it means the feature is still experimental, and I would prefer it not being merged to the main trunk (i.e., 'master' branch) yet.

However, If the PT-related changes are well encapsulated and won't affect any other parts of the code as long as `control.has_PT` is `false`, maybe we can let it be merged. Even in that case, the further development will have to be continued in a separate, properly named branch. 
created_at: 2025-04-06T20:35:18Z
author_association: CONTRIBUTOR
path: bc.cxx
user: login: sungho91
body: @echoi Let's leave PT as it is, since I believe the PT-related changes are well encapsulated and do not affect the original functionality of DES3D.
created_at: 2025-04-07T07:56:33Z
author_association: COLLABORATOR
path: examples/rate_and_state_friction/vel_neutral/stick_slip.cfg
user: login: sungho91
body: @echoi I changed the model name from stick_slip.cfg to rsf_tri_layer_shear_test_vel_weakening.cfg, and similar descriptive names for other cases. Also, I added Case 4 for the top boundary condition in bc.cxx, and modified input.cxx as it was originally restricted to only handle up to Case 3. And removing the patch file and restoring Makefile.


created_at: 2025-04-07T08:00:50Z
author_association: COLLABORATOR
path: 2vtk.py
user: login: sungho91
body: @echoi I've restored 2vtk.py.
created_at: 2025-04-07T08:01:51Z
author_association: COLLABORATOR
path: examples/rate_and_state_friction/vel_strengthening/rsf_tri_layer_shear_test_vel_strengthening.cfg
user: login: echoi
body: Shouldn't `vbc_z1` be `4`? 
created_at: 2025-04-09T03:13:44Z
author_association: CONTRIBUTOR
path: examples/rate_and_state_friction/vel_neutral/rsf_tri_layer_shear_test_vel_neutral.cfg
user: login: echoi
body: Shouldn't `vbc_z1` be `4`?
created_at: 2025-04-09T03:14:31Z
author_association: CONTRIBUTOR
path: 2vtk.py
user: login: echoi
body: Lines 628-629: When the option `-heat` is given, `output_heatflux` should be `True`.
created_at: 2025-04-09T03:17:32Z
author_association: CONTRIBUTOR
path: input.cxx
user: login: echoi
body: Line 283: A typo, `PT look` -> `PT loop`(?)
created_at: 2025-04-09T03:27:12Z
author_association: CONTRIBUTOR
path: input.cxx
user: login: echoi
body: @sungho91 @tan2 @chaseshyu `Makefile` is already written in such a way that when `adaptive_time_step` and `use_R_S` are both `1`, `ATS` and `RS` are defined during the build: i.e., `-DATS` and `-DRS` are added as compiler options. In this commit, however, Sungho newly introduced `control.has_ATS`. 

There are some merits in controlling the code selection using `#ifdef ATS/RS` at compile time. 

On the other hand, if we are going to allow the RS friction as one of the rheology types, allowing users to choose ATS at run time, through an input file, seems to be a better approach (BTW, ATS should be automatically enforced when RS friction is in use!).

I'm curious what is your preference. This commit might be just merged, but it would be helpful for future development if we reach an agreement on this code design issue.
created_at: 2025-04-09T03:46:20Z
author_association: CONTRIBUTOR
path: input.cxx
user: login: sungho91
body: @echoi @tan2 @chaseshyu 

I see the point of Eunseo. Personally, I think supporting ATS as a run-time option offers more flexibility, especially since we’re considering RS friction as a selectable rheology type. Automatically enforcing ATS when RS is active also makes logical sense to me.

If the performance differences are negligible or manageable, I believe run-time configurability is the better choice in the long run for usability and extensibility. That’s the rationale behind how I wrote the code in this commit.

That said, keeping the compile-time flags  for performance-critical builds—like Denis did—also seems like a reasonable compromise. Though, to be honest, I'm not sure if it actually improves performance meaningfully.
created_at: 2025-04-10T11:44:37Z
author_association: COLLABORATOR
path: input.cxx
user: login: chaseshyu
body: @echoi @sungho91 @tan2  In my opinion, the choice of an option loading a different linkage of libraries might need a flag during the compiling. In this case, if RS and ATS is no need addisional library for each feature, I prefer make option in the input file, just like `has_ATS`
created_at: 2025-04-10T19:02:41Z
author_association: MEMBER
path: input.cxx
user: login: echoi
body: Thanks for the opinions. Based on them, I presume RS and ATS compiler option can be removed in the future unless Eh has a different opinion. We can have another discussion when such a pull request is submitted. I'll go ahead and mark this thread as resolved.
created_at: 2025-04-11T16:00:38Z
author_association: CONTRIBUTOR

PR comments:
user: login: echoi
created_at: 2025-03-08T19:58:19Z
body: It seems to be too much to put three major feature upgrades in one pull request: poro-elasticity, pseudotransient method and rate-and-state friction. It takes long to go through all the changes in the first place and it will make it difficult to pinpoint a problematic commit in case of issues occurring downstream. Considering that these are independent (right?), I'd like to suggest breaking them into three separate pull requests.
author_association: CONTRIBUTOR
user: login: LucLavier
created_at: 2025-04-01T15:04:10Z
body: Sungho

  Could you resolve the issues for velocity neutral and strengthening?

Luc

From: Sungho Lee ***@***.***>
Date: Tuesday, April 1, 2025 at 9:59 AM
To: GeoFLAC/DynEarthSol ***@***.***>
Cc: Lavier, Luc L ***@***.***>, Review requested ***@***.***>
Subject: Re: [GeoFLAC/DynEarthSol] Implementation of rate-and-state friction model (PR #11)

@sungho91 commented on this pull request.

________________________________

On examples/rate_and_state_friction/vel_neutral/stick_slip.cfg<https://github.com/GeoFLAC/DynEarthSol/pull/11#discussion_r2023039525>:

Good point. However, I think I can fix the model names later, since I'm still working on the examples. There's still room for changes in those examples anyway.

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Reply to this email directly, view it on GitHub<https://github.com/GeoFLAC/DynEarthSol/pull/11#discussion_r2023039525>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/AUX7EVDESXR7FRW3ANPK4ID2XKSU7AVCNFSM6AAAAABYMHPAZCVHI2DSMVQWIX3LMV43YUDVNRWFEZLROVSXG5CSMV3GSZLXHMZDOMZTGAYTENRWGY>.
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author_association: NONE
user: login: echoi
created_at: 2025-04-11T16:22:43Z
body: @sungho91 I think all we need to do now is to resolve the conflicts with the current master. Please go ahead and resolve them. Then, this pull request will be merged.
author_association: CONTRIBUTOR
user: login: sungho91
created_at: 2025-04-15T11:12:21Z
body: @chaseshyu prepare_dhacc is a new thing included in your last commit? 
author_association: COLLABORATOR
user: login: chaseshyu
created_at: 2025-04-15T14:36:07Z
body: > @chaseshyu prepare_dhacc is a new thing included in your last commit?

No, I have removed this outdated function in [8bacfafa7e19647752eb7fbe2a388c6d70ff17eb].
author_association: MEMBER
user: login: sungho91
created_at: 2025-04-16T00:17:28Z
body: @echoi @tan2 — do you know how to resolve this build failure?

In my code, I still have the line prepare_dhacc(var.surfinfo);, but Chase removed some outdated functions here: [8bacfaf](https://github.com/GeoFLAC/DynEarthSol/commit/8bacfafa7e19647752eb7fbe2a388c6d70ff17eb)

I think my commit didn’t incorporate Chase’s update, which caused the build tests to fail.
author_association: COLLABORATOR
user: login: echoi
created_at: 2025-04-16T02:22:41Z
body: > > @chaseshyu prepare_dhacc is a new thing included in your last commit?
> 
> No, I have removed this outdated function in [[8bacfaf](https://github.com/GeoFLAC/DynEarthSol/commit/8bacfafa7e19647752eb7fbe2a388c6d70ff17eb)].

@chaseshyu , I still see many lines including `dhacc` in the latest commit on the master branch. Is this intentional or are they supposed to be cleaned up? 
author_association: CONTRIBUTOR
user: login: echoi
created_at: 2025-04-16T02:30:52Z
body: > @echoi @tan2 — do you know how to resolve this build failure?
> 
> In my code, I still have the line prepare_dhacc(var.surfinfo);, but Chase removed some outdated functions here: [8bacfaf](https://github.com/GeoFLAC/DynEarthSol/commit/8bacfafa7e19647752eb7fbe2a388c6d70ff17eb)
> 
> I think my commit didn’t incorporate Chase’s update, which caused the build tests to fail.

@sungho91 I just committed a small set of changes and the latest commit now passes all three build tests. It might be ready for merging but the system says, "New changes require approval from someone other than the last pusher." and I'm the last pusher. Let's ask @tan2 if he can approve merging.
author_association: CONTRIBUTOR
user: login: tan2
created_at: 2025-04-16T03:52:06Z
body:  I approved the pull request but still encounter this error:This branch
cannot be rebased due to conflicts

On Wed, Apr 16, 2025 at 10:31 AM Eunseo Choi ***@***.***>
wrote:

> @echoi <https://github.com/echoi> @tan2 <https://github.com/tan2> — do
> you know how to resolve this build failure?
>
> In my code, I still have the line prepare_dhacc(var.surfinfo);, but Chase
> removed some outdated functions here: 8bacfaf
> <https://github.com/GeoFLAC/DynEarthSol/commit/8bacfafa7e19647752eb7fbe2a388c6d70ff17eb>
>
> I think my commit didn’t incorporate Chase’s update, which caused the
> build tests to fail.
>
> @sungho91 <https://github.com/sungho91> I just committed a small set of
> changes and the latest commit now passes all three build tests. It might be
> ready for merging but the system says, "New changes require approval from
> someone other than the last pusher." and I'm the last pusher. Let's ask
> @tan2 <https://github.com/tan2> if he can approve merging.
>
> —
> Reply to this email directly, view it on GitHub
> <https://github.com/GeoFLAC/DynEarthSol/pull/11#issuecomment-2808049346>,
> or unsubscribe
> <https://github.com/notifications/unsubscribe-auth/AABWQQWEI5YJTFUDF4K7TCD2ZW6HHAVCNFSM6AAAAABYMHPAZCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDQMBYGA2DSMZUGY>
> .
> You are receiving this because you were mentioned.Message ID:
> ***@***.***>
> *echoi* left a comment (GeoFLAC/DynEarthSol#11)
> <https://github.com/GeoFLAC/DynEarthSol/pull/11#issuecomment-2808049346>
>
> @echoi <https://github.com/echoi> @tan2 <https://github.com/tan2> — do
> you know how to resolve this build failure?
>
> In my code, I still have the line prepare_dhacc(var.surfinfo);, but Chase
> removed some outdated functions here: 8bacfaf
> <https://github.com/GeoFLAC/DynEarthSol/commit/8bacfafa7e19647752eb7fbe2a388c6d70ff17eb>
>
> I think my commit didn’t incorporate Chase’s update, which caused the
> build tests to fail.
>
> @sungho91 <https://github.com/sungho91> I just committed a small set of
> changes and the latest commit now passes all three build tests. It might be
> ready for merging but the system says, "New changes require approval from
> someone other than the last pusher." and I'm the last pusher. Let's ask
> @tan2 <https://github.com/tan2> if he can approve merging.
>
> —
> Reply to this email directly, view it on GitHub
> <https://github.com/GeoFLAC/DynEarthSol/pull/11#issuecomment-2808049346>,
> or unsubscribe
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author_association: MEMBER
user: login: echoi
created_at: 2025-04-16T08:39:18Z
body: I see the green merge button with three options. Rebase and merge is one of them. Since it doesn't work, I will try one of the others.
author_association: CONTRIBUTOR
user: login: chaseshyu
created_at: 2025-04-16T17:52:09Z
body: > > @echoi @tan2 — do you know how to resolve this build failure?
> > In my code, I still have the line prepare_dhacc(var.surfinfo);, but Chase removed some outdated functions here: [8bacfaf](https://github.com/GeoFLAC/DynEarthSol/commit/8bacfafa7e19647752eb7fbe2a388c6d70ff17eb)
> > I think my commit didn’t incorporate Chase’s update, which caused the build tests to fail.
> 
> @sungho91 I just committed a small set of changes and the latest commit now passes all three build tests. It might be ready for merging but the system says, "New changes require approval from someone other than the last pusher." and I'm the last pusher. Let's ask @tan2 if he can approve merging.

@echoi, `dhacc` is for surface marker correction at `steps%quality_check_step_interval = 0`. So, before every time remesh, surface marker is always corrected and `dhacc` becomes zero. Therefore remesh interpolation is not needed for `dhacc` and I remove it in remesh.
author_association: MEMBER
user: login: LucLavier
created_at: 2025-10-14T15:09:26Z
body: Super-convergence/MND improve reconstruction quality of strains/stresses, but they don’t change the discrete coupling between volumetric stress (pressure-like DOF) and velocity/displacement that causes checkerboards on collocated layouts. FLAC’s fix is in the stencil itself: move the pressure information through a mixed (nodal/zone) pathway so adjacent zones aren’t free to oscillate (+p,−p,+p,−p,… )(+p,-p,+p,-p,\dots)(+p,−p,+p,−p,…) at zero force.


  I tried to work with CHatGPT to work on a fix. It should work since it is the same as ITASCA. It is is in teh attached PDF

author_association: NONE
user: login: tan2
created_at: 2025-10-14T15:31:21Z
body: Luc,

You forgot to attach the pdf in your email.

Eh Tan
Sent from mobile phone

LucLavier ***@***.***> 於 2025年10月14日 週二 23:09 寫道：

> *LucLavier* left a comment (GeoFLAC/DynEarthSol#11)
> <https://github.com/GeoFLAC/DynEarthSol/pull/11#issuecomment-3402343663>
> Super-convergence/MND improve reconstruction quality of strains/stresses,
> but they don’t change the discrete coupling between volumetric stress
> (pressure-like DOF) and velocity/displacement that causes checkerboards on
> collocated layouts. FLAC’s fix is in the stencil itself: move the pressure
> information through a mixed (nodal/zone) pathway so adjacent zones aren’t
> free to oscillate (+p,−p,+p,−p,… )(+p,-p,+p,-p,\dots)(+p,−p,+p,−p,…) at
> zero force.
>
>
> I tried to work with CHatGPT to work on a fix. It should work since it is
> the same as ITASCA. It is is in teh attached PDF
>
> —
> Reply to this email directly, view it on GitHub
> <https://github.com/GeoFLAC/DynEarthSol/pull/11#issuecomment-3402343663>,
> or unsubscribe
> <https://github.com/notifications/unsubscribe-auth/AABWQQSPNXMUGHZBTRACSFT3XUG3ZAVCNFSM6AAAAABYMHPAZCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZTIMBSGM2DGNRWGM>
> .
> You are receiving this because you were mentioned.Message ID:
> ***@***.***>
>

author_association: MEMBER
user: login: LucLavier
created_at: 2025-10-14T15:50:26Z
body: 

From: Eh Tan ***@***.***>
Date: Tuesday, October 14, 2025 at 10:32 AM
To: GeoFLAC/DynEarthSol ***@***.***>
Cc: Lavier, Luc L ***@***.***>, Review requested ***@***.***>
Subject: Re: [GeoFLAC/DynEarthSol] Implementation of rate-and-state friction model (PR #11)
[Image removed by sender.]tan2 left a comment (GeoFLAC/DynEarthSol#11)<https://github.com/GeoFLAC/DynEarthSol/pull/11#issuecomment-3402440031>
Luc,

You forgot to attach the pdf in your email.

Eh Tan
Sent from mobile phone

LucLavier ***@***.***> 於 2025年10月14日 週二 23:09 寫道：

> *LucLavier* left a comment (GeoFLAC/DynEarthSol#11)
> <https://github.com/GeoFLAC/DynEarthSol/pull/11#issuecomment-3402343663>
> Super-convergence/MND improve reconstruction quality of strains/stresses,
> but they don’t change the discrete coupling between volumetric stress
> (pressure-like DOF) and velocity/displacement that causes checkerboards on
> collocated layouts. FLAC’s fix is in the stencil itself: move the pressure
> information through a mixed (nodal/zone) pathway so adjacent zones aren’t
> free to oscillate (+p,−p,+p,−p,… )(+p,-p,+p,-p,\dots)(+p,−p,+p,−p,…) at
> zero force.
>
>
> I tried to work with CHatGPT to work on a fix. It should work since it is
> the same as ITASCA. It is is in teh attached PDF
>
> —
> Reply to this email directly, view it on GitHub
> <https://github.com/GeoFLAC/DynEarthSol/pull/11#issuecomment-3402343663>,
> or unsubscribe
> <https://github.com/notifications/unsubscribe-auth/AABWQQSPNXMUGHZBTRACSFT3XUG3ZAVCNFSM6AAAAABYMHPAZCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZTIMBSGM2DGNRWGM>
> .
> You are receiving this because you were mentioned.Message ID:
> ***@***.***>
>

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author_association: NONE
user: login: LucLavier
created_at: 2025-10-14T16:09:41Z
body: Sungho et al

    I have gone back through the results and notes I have taken in the past. I compiled all of it in a set of changes that should make rate and sate with Mohr Coulomb possible. There are several modifications some of which I believe we have already such as the way we map the aging law to Mohr Coulomb (2), the Rayleigh damping with alpha = 0 in the fault zone and the time step resolution. We do not have the Perzyna regularization that would regularize the plastic behavior and the velocity, the damping schedule for the fault zone, and the optional micro viscosity if needed.  We can try the absorbing boundary conditions later if needed.

  Cheers Luc

From: Eh Tan ***@***.***>
Date: Tuesday, October 14, 2025 at 10:32 AM
To: GeoFLAC/DynEarthSol ***@***.***>
Cc: Lavier, Luc L ***@***.***>, Review requested ***@***.***>
Subject: Re: [GeoFLAC/DynEarthSol] Implementation of rate-and-state friction model (PR #11)
[Image removed by sender.]tan2 left a comment (GeoFLAC/DynEarthSol#11)<https://github.com/GeoFLAC/DynEarthSol/pull/11#issuecomment-3402440031>
Luc,

You forgot to attach the pdf in your email.

Eh Tan
Sent from mobile phone

LucLavier ***@***.***> 於 2025年10月14日 週二 23:09 寫道：

> *LucLavier* left a comment (GeoFLAC/DynEarthSol#11)
> <https://github.com/GeoFLAC/DynEarthSol/pull/11#issuecomment-3402343663>
> Super-convergence/MND improve reconstruction quality of strains/stresses,
> but they don’t change the discrete coupling between volumetric stress
> (pressure-like DOF) and velocity/displacement that causes checkerboards on
> collocated layouts. FLAC’s fix is in the stencil itself: move the pressure
> information through a mixed (nodal/zone) pathway so adjacent zones aren’t
> free to oscillate (+p,−p,+p,−p,… )(+p,-p,+p,-p,\dots)(+p,−p,+p,−p,…) at
> zero force.
>
>
> I tried to work with CHatGPT to work on a fix. It should work since it is
> the same as ITASCA. It is is in teh attached PDF
>
> —
> Reply to this email directly, view it on GitHub
> <https://github.com/GeoFLAC/DynEarthSol/pull/11#issuecomment-3402343663>,
> or unsubscribe
> <https://github.com/notifications/unsubscribe-auth/AABWQQSPNXMUGHZBTRACSFT3XUG3ZAVCNFSM6AAAAABYMHPAZCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZTIMBSGM2DGNRWGM>
> .
> You are receiving this because you were mentioned.Message ID:
> ***@***.***>
>

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author_association: NONE
user: login: LucLavier
created_at: 2025-10-14T16:10:22Z
body: Sorry I forgot the pdf attachment again.

From: Lavier, Luc L ***@***.***>
Date: Tuesday, October 14, 2025 at 11:09 AM
To: GeoFLAC/DynEarthSol ***@***.***>, GeoFLAC/DynEarthSol ***@***.***>
Cc: Review requested ***@***.***>
Subject: Mohr-Coulomb for Sungho
Sungho et al

    I have gone back through the results and notes I have taken in the past. I compiled all of it in a set of changes that should make rate and sate with Mohr Coulomb possible. There are several modifications some of which I believe we have already such as the way we map the aging law to Mohr Coulomb (2), the Rayleigh damping with alpha = 0 in the fault zone and the time step resolution. We do not have the Perzyna regularization that would regularize the plastic behavior and the velocity, the damping schedule for the fault zone, and the optional micro viscosity if needed.  We can try the absorbing boundary conditions later if needed.

  Cheers Luc

From: Eh Tan ***@***.***>
Date: Tuesday, October 14, 2025 at 10:32 AM
To: GeoFLAC/DynEarthSol ***@***.***>
Cc: Lavier, Luc L ***@***.***>, Review requested ***@***.***>
Subject: Re: [GeoFLAC/DynEarthSol] Implementation of rate-and-state friction model (PR #11)
[Image removed by sender.]tan2 left a comment (GeoFLAC/DynEarthSol#11)<https://github.com/GeoFLAC/DynEarthSol/pull/11#issuecomment-3402440031>
Luc,

You forgot to attach the pdf in your email.

Eh Tan
Sent from mobile phone

LucLavier ***@***.***> 於 2025年10月14日 週二 23:09 寫道：

> *LucLavier* left a comment (GeoFLAC/DynEarthSol#11)
> <https://github.com/GeoFLAC/DynEarthSol/pull/11#issuecomment-3402343663>
> Super-convergence/MND improve reconstruction quality of strains/stresses,
> but they don’t change the discrete coupling between volumetric stress
> (pressure-like DOF) and velocity/displacement that causes checkerboards on
> collocated layouts. FLAC’s fix is in the stencil itself: move the pressure
> information through a mixed (nodal/zone) pathway so adjacent zones aren’t
> free to oscillate (+p,−p,+p,−p,… )(+p,-p,+p,-p,\dots)(+p,−p,+p,−p,…) at
> zero force.
>
>
> I tried to work with CHatGPT to work on a fix. It should work since it is
> the same as ITASCA. It is is in teh attached PDF
>
> —
> Reply to this email directly, view it on GitHub
> <https://github.com/GeoFLAC/DynEarthSol/pull/11#issuecomment-3402343663>,
> or unsubscribe
> <https://github.com/notifications/unsubscribe-auth/AABWQQSPNXMUGHZBTRACSFT3XUG3ZAVCNFSM6AAAAABYMHPAZCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZTIMBSGM2DGNRWGM>
> .
> You are receiving this because you were mentioned.Message ID:
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>

—
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author_association: NONE
user: login: echoi
created_at: 2025-10-15T21:27:31Z
body: Hi Luc,

This pull request thread has been closed. Please continue the discussion
regarding R&S friction at [
https://github.com/GeoFLAC/DynEarthSol/issues/6](https://github.com/GeoFLAC/DynEarthSol/issues/6)
.
Please upload your work done with ChatGPT there with any demo model results.
You've acknowledged the missing PDF attachment but I still can't find it
anywhere...

Best,
Eunseo

On Tue, Oct 14, 2025 at 11:10 AM LucLavier ***@***.***> wrote:

> *LucLavier* left a comment (GeoFLAC/DynEarthSol#11)
> <https://github.com/GeoFLAC/DynEarthSol/pull/11#issuecomment-3402644605>
> Sorry I forgot the pdf attachment again.
>
> From: Lavier, Luc L ***@***.***>
> Date: Tuesday, October 14, 2025 at 11:09 AM
> To: GeoFLAC/DynEarthSol ***@***.***>, GeoFLAC/DynEarthSol ***@***.***>
> Cc: Review requested ***@***.***>
> Subject: Mohr-Coulomb for Sungho
> Sungho et al
>
> I have gone back through the results and notes I have taken in the past. I
> compiled all of it in a set of changes that should make rate and sate with
> Mohr Coulomb possible. There are several modifications some of which I
> believe we have already such as the way we map the aging law to Mohr
> Coulomb (2), the Rayleigh damping with alpha = 0 in the fault zone and the
> time step resolution. We do not have the Perzyna regularization that would
> regularize the plastic behavior and the velocity, the damping schedule for
> the fault zone, and the optional micro viscosity if needed. We can try the
> absorbing boundary conditions later if needed.
>
> Cheers Luc
>
> From: Eh Tan ***@***.***>
> Date: Tuesday, October 14, 2025 at 10:32 AM
> To: GeoFLAC/DynEarthSol ***@***.***>
> Cc: Lavier, Luc L ***@***.***>, Review requested ***@***.***>
> Subject: Re: [GeoFLAC/DynEarthSol] Implementation of rate-and-state
> friction model (PR #11)
> [Image removed by sender.]tan2 left a comment (GeoFLAC/DynEarthSol#11)<
> https://github.com/GeoFLAC/DynEarthSol/pull/11#issuecomment-3402440031>
> Luc,
>
> You forgot to attach the pdf in your email.
>
> Eh Tan
> Sent from mobile phone
>
> LucLavier ***@***.***> 於 2025年10月14日 週二 23:09 寫道：
>
> > *LucLavier* left a comment (GeoFLAC/DynEarthSol#11)
> > <https://github.com/GeoFLAC/DynEarthSol/pull/11#issuecomment-3402343663>
>
> > Super-convergence/MND improve reconstruction quality of
> strains/stresses,
> > but they don’t change the discrete coupling between volumetric stress
> > (pressure-like DOF) and velocity/displacement that causes checkerboards
> on
> > collocated layouts. FLAC’s fix is in the stencil itself: move the
> pressure
> > information through a mixed (nodal/zone) pathway so adjacent zones
> aren’t
> > free to oscillate (+p,−p,+p,−p,… )(+p,-p,+p,-p,\dots)(+p,−p,+p,−p,…) at
> > zero force.
> >
> >
> > I tried to work with CHatGPT to work on a fix. It should work since it
> is
> > the same as ITASCA. It is is in teh attached PDF
> >
> > —
> > Reply to this email directly, view it on GitHub
> > <https://github.com/GeoFLAC/DynEarthSol/pull/11#issuecomment-3402343663>,
>
> > or unsubscribe
> > <
> https://github.com/notifications/unsubscribe-auth/AABWQQSPNXMUGHZBTRACSFT3XUG3ZAVCNFSM6AAAAABYMHPAZCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZTIMBSGM2DGNRWGM>
>
> > .
> > You are receiving this because you were mentioned.Message ID:
> > ***@***.***>
> >
>
> —
> Reply to this email directly, view it on GitHub<
> https://github.com/GeoFLAC/DynEarthSol/pull/11#issuecomment-3402440031>,
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> https://github.com/notifications/unsubscribe-auth/AUX7EVG7XUFJTWXAQMJB5XL3XUJR7AVCNFSM6AAAAABYMHPAZCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZTIMBSGQ2DAMBTGE>.
>
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author_association: CONTRIBUTOR