PlasticityNet: Learning to Simulate Metal, Sand, and Snow for Optimization Time Integration
Anonymous Author(s)
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Abstract
In this paper, we propose a neural network-based approach for learning to representthe behavior of plastic solid materials ranging from rubber and metal to sand andsnow.
Unlike elastic forces such as spring forces, these plastic forces do not resultfrom the positional gradient of any potential energy, imposing great challenges onthe stability and flexibility of their simulation.
Our method effectively resolves thisissue by learning a generalizable plastic energy whose derivative closely matchesthe analytical behavior of plastic forces.
Our method, for the first time, enablesthe simulation of a wide range of arbitrary elasticity-plasticity combinations usingtime step-independent, unconditionally stable optimization-based time integrators.
We demonstrate the efficacy of our method by learning and producing challenging2D and 3D effects of metal, sand, and snow with complex dynamics.
Introduction
Combining machine learning with physical simulations has recently attracted a lot of attention.
Avast amount of existing research adopts an end-to-end approach, where the specific underlying computational physics system is treated as a black box [39, 35].
Harnessing the power of neural networks,this research has been successfully applied in computer animation [10], multibody systems [3, 6, 52],human musculature simulation [17], computational fluid dynamics [4, 12], and non-linear continuum mechanics [5].
An alternative direction is represented by physics-informed neural networks(PINN) [37, 18], where in its original form, the residual of a partial differential equation is directlyused as the loss function so that the network training is a physics-aware learning process.
PINNbecomes powerful when the design space of the input to the network can be parameterized, whichaccelerates both the roll-out and the inverse optimization process [44].
Another noteworthy categoryis learning the physical modeling where the machine can either help increase the model resolution ina coarser grid [21], inject nonlinearity to a linear model [32], or apply a learnable model reduction toreduce the system degrees-of-freedom (DOF) for acceleration [40, 41].
Despite its great success, training a neural network to replace a traditional simulator is not alwaysthe preferred choice.
This is partially due to the challenges in the trained model’s generality andportability.
For example, a trained model on a particle-based deformable body solver (such as the
Material Point Method (MPM [16]) cannot be directly applied to the mesh-based Finite Element
Method (FEM) [42], while in traditional continuum mechanics, the constitutive model that describesthe relationship between force and deformation is an independent module from the underlyinggeometric description or simulation scheme.
Indeed, by simply switching the constitutive model andapplying minor changes to the existing and general simulation pipeline, a wide range of materialscan be simulated in the same framework, ranging from sand [36, 9, 20, 45] and metal [33], to snow[14, 50, 31] and glacier [51].
Many elastic materials, including those represented by mass-spring systems [2] and common hyperelastic solids [43], are usually governed by analytical elastic potential energy functions in terms ofthe deformation.
These models are well fitted to experiments and proven to be simple, accurate, andpredictive.
Although most of these energy functions are highly nonlinear and non-convex, reformulating the dynamic simulation process as a numerical optimization problem and solving it using linesearches can guarantee global convergence to a solution [25].
Beyond hyperelasticity, plasticity ismuch more challenging.
The mechanical response of plastic materials imposes extra difficulties inthe implementation as it is path-dependant and non-smooth.
One common handling of plasticity isthe return mapping algorithm, which applies the effects of plastic deformation to the elastic forces.
However, this leads to asymmetrical force derivatives, which eliminate the possibility of integratingthe plasticity into the energy function in a single optimization and complicates the pipeline.
In therecent work of Energetic Consistent Inelasticity (ECI) [30], the plasticity is analytically modeled asan energy functional, and the simulation can be formulated as an optimization problem just like simulating pure elastic materials.
However, their analytical derivation only works for St.Venant-Kirchhoff(StVK) elasticity with the von-Mises plasticity.
In this work, we propose PlasticityNet, a neural network-based approach for learning an energybased force that locally approximates elastic forces with plasticity models and is compatible withoptimization time integrators.
PlasticityNet framework supports any combinations of elastic modelsand plastic models and works with both MPM and FEM discretizations.
With optimization timeintegrators, we demonstrate that our framework can simulate vast types of plasticities, such as metal,sand, and snow, with large time step sizes.
Related work
Classic Plasticity Models The classic plastic models utilized the geometry information of the plasticity and are available for many applications.
In the computer graphics community, researchers havefollowed mechanical literature on the Drucker-Prager elastoplasticity model [36, 9], and developedparticle-based simulations of dry [20] and wet [45] sand.
Extending a similar Cam-Clay plasticitymodel, snow avalanches [14, 31], glacier calving [51] and food fracturing [50] are also capturedwith high visual plausibility as well as physical accuracy.
For metals and dough-like materials,the von-Mises plasticity model [33] is usually adopted, while [49, 15] presented its anisotropicextensions.
Still, the implementation of these models in modern, optimization-based simulators iscumbersome due to the non-integrable forces.
Recently, [30] proposed an elastoplastic energy ofvon-
Mises plasticity under StVK elasticity for optimization time integrator, which can be viewedas a special-case analytical solution to our framework under the same combination of elasticity andplasticity.
But our framework works for arbitrary combinations.
Data-Driven Plasticity Models
The machine learning approach has been used to find new plasticmodels using large sets of measurements and parameters, outperforming many long-standing handcrafted models.
The macro-level constitutive relationship is learned from the results of the micro-levelsimulations [34, 38].
A similar approach is applied in [46] to learn anisotropic hyperelasticity, whereadditional geometrical information is included in the input.
PINN can also be applied in plastic modelfinding from experimental measurements [1, 47, 22], where the loss includes the stress and Hessian,to infer stress with more accuracy in the implicit simulators.
However, there does not exist any priorwork, to the best of the author’s knowledge, that tried to find variational form for arbitrary plasticitymodel.
Optimization Time Integration
The optimization time integrators have advantages in termsof stability under large deformations and large time step sizes.
Many of the nonlinear systemsof equations that arise from implicit simulation can be integrated to get equivalent optimizationproblems, which allow robust optimization techniques to be applied.
The MPM simulator in thiswork is based on [13], which formulated the backward Euler time integration with hyperelasticmaterials as a minimization problem.
[27] and [48] also explored domain decompositions andhiearachical preconditioners to improve robustness and efficiency.
The FEM simulator in this work isbased on Incremental Potential Contact (IPC) [26], which proposed a variational form for frictionalcontacts.
Their optimization-based frictional contact framework was also extended to codimensionalobjects [28], rigid bodies [11, 23], articulated multibodies [7], reduced elastic solids [24], embeddedinterfaces [53], and FEM-MPM coupled domains [29].
Background3.1 Optimization Time Integration
In this section, we briefly introduce the optimization time integration for elastodynamics simulationswith the Material Point Method (MPM) and the Finite Element Method (FEM).
We refer the readersto [25] and [13] for more details.
FEM discretizes the simulation domain as unstructured meshes (e.g., triangle meshes in 2D), while in
MPM, a point cloud composed of material particles is used to discretize the domain.
While FEMdirectly uses the mesh nodes as the simulation degrees-of-freedom (DOF), MPM transfers its particlestate to a uniform background grid, whose nodes are used as the DOFs for the integration of forces[16].
Robust simulation of elastodynamics can be achieved via implicit time integration, whichupdates the nodal positions ( x ) or velocities ( v ) step by step based on the previous physical states.
Tostep from t n to t n +1 = t n + ∆ t with time step size ∆ t , with implicit Euler time integration rule, oneneeds to solve a nonlinear system of equations
[Equation]
Here v is the velocity DOF formed by concatenating all nodal velocity vectors, similarly concatenated,
M is the mass matrix, g is the gravitational acceleration vector, and f is the internal force vector.
Without plasticity, the internal force on a node i can be calculated as
[Equation]
where q iterates the surrounding elements/particles of node i in FEM/MPM, V 0 q is the initial volumeof the element/particle, F = ( I + ∆ t ∇ v ) F n (MPM) or F = ∇ x n + ∆ t ∇ v (FEM) is the deformationgradient, which measures deformation from the undeformed state to the deformed state, and P is thefirst-Piola Kirchhoff stress, which describes the internal force per unit area within a material.
∇ w iqis the gradient of the weight function on node i evaluated on an element/particle center.
The weightfunction is for transferring physical quantities between the elements/particles and the mesh/gridnodes.
Unlike FEM, the last time step is used in MPM as the reference configuration, and so ∇ w iq iscalculated as F n ⊤ q ∇ w niq .
When there exists an energy density function Ψ
such that P ( F ) = ∂
Ψ ∂ F , solving Equation 1 is equivalentto solving the following optimization problem
[Equation]
This formulation is more favored because with line search methods, convergence to a local minimumof Equation 3 can be guaranteed even when simulating challenging cases with stiff materials or largetime step sizes.
After solving for the velocity v n +1 , FEM directly updates mesh nodal positionsas x n +1 = x n + ∆ t v n +1 , while for MPM, the velocity on the grid node is interpolated to particlelocations for particle advection.
The background grid is reset at the beginning of each time step,which allows MPM to benefit from the conveniences of a regular grid and a mesh-free formulation atthe cost of some accuracy loss due to the transfers between the grid and particles.
3.2 Return Mapping for Plasticity
[Figure]
With plasticity, objects can undergo both plastic and elastic deformations,and the deformation gradient at the current time step can be decomposedas
[Equation]
based on the finite strain theory.
Here F P,n +1 encodes the permanentplastic deformation of the rest shape, and F E,n +1 is the elastic deformation that results in effective elastic forces.
In theory, F E,n +1 is constrained within certain elastic regions.
Computation-wise, an elasticpredictor F E,tr = F n +1 ( F P,n ) − 1 can be computed first by assuming
F P,n +1 = F P,n .
If F E,tr is outside the elastic region, it will be projected back onto the boundary ofthe region to obtain F E,n +1 =
Z ( F E,tr ) (Figure 1).
This projection Z is called a return mapping .
Within this framework, the implicit elastoplastic nodal force can be computed as [30]
[Equation]
where τ ( F ) = P ( F ) F ⊤ is the Kirchoff stress.
The above forces are integrable only if τ ( Z ( F ))
F −⊤can be represented as the gradient of some energy function:
[Equation]
Most combinations of elastic constitutive models and plastic return mappings do not satisfy thisintegrability condition because the Jacobian field of the right-hand side is asymmetrical.
Note thatdirectly feeding Z ( F ) into an elastic potential does not form a potential energy for the elastoplasticforces defined in Equation 5.
[30] only found one specific combination such that an elastoplasticpotential energy exists.
Thus, it remains challenging to simulate versatile plastic behaviors withoptimization time integrators and achieve robust performance.
PlasticityNet
We propose PlasticityNet, a neural network-based elastoplastic model that finds a family of localpotential energies whose negative gradients can approximate the elastoplastic forces within a smallneighborhood so that plasticity can be conveniently simulated using optimization time integrators.
The model architecture is illustrate in Figure 2.
Specifically, instead of finding a global energyfunction Ψ( F ) , we search for an energy Ψ( F , F 0 ) , parameterized by F 0 , such that
[Equation]
and
[Equation]
To exactly enforce the first equality, we propose the following linear correction:
[Equation] [Equation]
Here A ⊙ B =
A ij B ij = tr ( A ⊤ B ) is the matrix inner product.
It can be verified that
[Equation]
Then we only need to focus on the approximation part in Equation 7.
We design the training lossfunction for our neural network as
[Equation]
During training, F is only sampled near F 0 .
Please refer to Section 5.1 for details.
4.1 Hardening of Plasticity
Hardening effects are widely observed in metals and snow.
With hardening, the elastic region will expand by a certain amount whenever F E,trfalls in the plastic region.
To account for hardening, the return mapping
Z ( F , h ) and the energy Ψ
θ ( F , F 0 , h ) will depend on an extra hardeningstate h , which controls the shape of the elastic region.
This hardeningstate is a function of F .
However, to maintain integrability with respect to
F , we approximately update h based on F 0 , which is assumed to be closeto F .
4.2 Optimization Time Integration with PlasticityNet
Fixed-Point Iteration
The gradient of our learned elastoplastic potential energy Ψ θ ( F , F 0 ) onlyapproximates the effective stresses locally near F 0 .
To approach the accurate solution of Equation 1 with elastoplastic forces, we apply a fixed-point iteration on F 0 to let it converge to F n +1 .
Specifically,we solve a sequence of optimization problems
[Equation]
for j =
[Equation]
treating the concatenated deformation gradients F j 0 and hardening states h as constants, which areonly updated before each optimization as F j 0 = F ( v n + i,j ) and
h = h ( F 0 ) .
At convergence, we willobtain the true solution of Equation 1.
In practice, a few number of fixed-point iterations can alreadygenerate high-quality results.
Stability Regularizer We augment our learned potential with an extra quadratic regularizer tostabilize the simulation especially when the material is stiff or the time step size is large:
[Equation]
Here µ is the shear modulus of the material that Ψ θ is learning.
Note that this extra term is addedafter the model is trained instead of during the training.
This extra term does not change the gradientat F 0 , so it will not change the fixed point of Procedure 10.
Please see Section 5.3 for a comparisonbetween simulations with and without this regularizer.
4.3 Learning Volume-Preserving Return Mapping
[Figure]
The return mapping Z required by PlasticityNet can be either givenanalytically or learned.
Note that with different combinations ofmany practical elasticity and plasticity models, the return mappingmay not have a closed-form solution, and the projection can only beperformed by solving a nonlinear system of equations.
Here we provide a simple approach to learn a volume-preservingreturn mapping, which ensures that det( Z ( F ))
= det( F ) .
Forisotropic materials, the projection can be performed in the diagonal space, i.e., with F = U Diag( Σ ) V ⊤ being the singular valuedecomposition of F ; the projection is only needed for Σ .
In thediagonal space, a volume-preserving path is a straight line in the
Hencky strain (defined as ϵ = log( Σ ) ) space, which is perpendicularto the diagonal line.
The direction of the projection path is ˆ ϵ =
ϵ − sum( ϵ ) 1 .
The volume-preservingprojection in the Hencky strain can be unified by H =
ϵ − δγ ˆ ϵ ∥ ˆ ϵ ∥ for some δγ , with Z Σ ( Σ ) =
exp( H )and Z ( F ) =
U Diag( Z Σ ) V ⊤ .
An illustration is shown in Figure 3.
The elastic region is usually represented by an implicit function y ( Σ ) ≤ 0 .
We can use a neuralnetwork to predict δγ , where the training leverages the differentiability of the implicit representationfor the elastic region boundary.
The volume-preserving path usually has two intersections with theelastic region boundary.
To eliminate this ambiguity, we clamp the output of the neural network witha maximum ∥ ˆ ϵ ∥ .
We define our neural-network-based return mapping on the diagonal space as:
[Equation]
The training loss function for a single Σ is defined as
[Equation]
Here, the first term is to pull the points outside the elastic region back onto the boundary.
The secondterm is to avoid these points to be always projected onto the diagonal due to the clamping in δγ
[Figure]
Metal
Experiments
[Figure]
We show examples to demonstrate the capability of our PlasticityNet in learning versatile plasticitymodels.
Our physical simulators are implemented using C++, and we applied PyTorch to learn thepotential energies, which are then loaded into our simulators with TorchScript.
All our potentialenergies are trained as multilayer perceptrons using the Adam optimizer [19] on a single Nvidia RTX3090 GPU.
Please see Appendix A.1 for more training details.
All ground-truth data are generatedusing standard explicit time integration with analytical plasticity returning mapping under small timestep sizes for stability.
With our PlasticityNet, we can robustly simulate elastoplastic behaviors withmuch larger time step sizes using optimization time integrators.
5.1 Training
[Figure] The training of PlasticityNet only requires the return mapping (either given analytically or pre-trained)for the plasticity model and the Kirchhoff stress for the underlying elasticity model.
There is no needfor extra labeled data.
At each epoch, we will sample a new batch of ( F , F 0 , h ) .
The sampling ofdeformation gradients is based on its singular value decomposition F = U Diag( Σ ) V ⊤ , with U , Vbeing two rotation matrices.
To sample F and F 0 so that their singular values are close to each other,we set F
=
R 1 Diag( e ϵ ) R 2 and F = R
Diag( e ϵ + δ ϵ ) R 4 , where ϵ is a randomly sampled vector, δ ϵis a random perturbation, and R i ’s are randomly sampled rotation matrices.
The hardening state issampled uniformly from an appropriate range depending on the plasticity model.
Please see Appendix
A.2 for definitions of hardening states and their range selections.
In this work, we uniformly sampleϵ from [ − 1 , 1] d , δ ϵ from [ − 0 .
1] d for sand plasticity and metal plasticity, and [ − 0 .
2] d for thesnow plasticity.
The training loss curves of our 2D models are shown in Figure 4.
5.2 Testing on 2D Simulations
[Figure]
Sand Plasticity We start by learning the elastoplastic model of dry sand (Figure 5).
The model combination is St. Venant-Kirchhoff (StVK) elasticity,and the closed-form Drucker Prager plasticity returnmapping [20] (See Appendix A.2.1).
In this example,we simulate a column of sand falling onto the groundunder gravity with MPM.
Our method generates visually identical results compared to the ground truth,both with the same time step size and a 100 × largertime step size.
The quantitative comparison of ourresults and the ground truth is shown in Figure 9a.
Note that there is no hardening mechanism in thisplasticity model, so our PlasticityNet does not needthe hardening state in its input.
[Figure]
Snow Plasticity with Hardening Snow is anelastoplastic material that can become stiffer undercompression.
Essentially, this is the effect of hardening where its elastic region get expanded.
Thevariation in the stiffness across the snow body makesit easily fracture.
Here we simulate a snowball hitting Sand (a) 0. 1.0 Simulation Time [s] 0.
I o U
Snow [Figure] Metal (c) the ground in the MPM simulator (Figure 6). We use Neo-Hookean elasticity with the closed-formnon-associative Cam-Clay plasticity return mapping [14] (See Appendix A.2.2).
Our method generates similar results compared to the ground truth when using the same time step size.
The quantitativecomparison of our results and the ground truth is shown in Figure 9b.
Our framework remains stableeven under much larger time step sizes.
However, more numerical damping artifacts are introducedas the time step size increases, which results in slightly different behaviors compared to the groundtruth.
[Figure]
Metal Plasticity with Hardening Metal is another common plastic material with hardening.
In this example, wetrain PlasticityNet to learn metal plasticity with the StVKelasticity and the closed-form von-Mises plasticity returnmapping [33].
(See Appendix A.2.3)
We simulate a metalframe compressed by a rigid plate in the FEM simulator(Figure 7), where the Incremental Potential Contact (IPC)[26] is used to handle the frictional contact between thesolids.
When we run the explicit time integration to generate ground truth, we have to decrease Young’s modulusto enable using large enough time step sizes so that thesimulation can be finished in practical time.
Our methodwith the original setting generates visually identical results using a much large time step size.
The quantitativecomparison of our result and the ground truth is shown in
Figure 9c.
Metal Plasticity Return Mapping
Here we show anexample simulated using PlasticityNet with a learned vonMises plasticity return mapping.
The underlying elasticityis neo-Hookean, instead of the StVK model in the last example (See Appendix A.2.4).
Note that for Neo-Hookeanmaterial, there is no closed-form solution available for thevon-Mises return mapping.
In this case, a nonlinear optimization problem will need to be solved toperform the return mapping for every element/particle in every time step, which could severely slowdown the standard explicit time integration.
Using the same parameters as the metal compressionexperiments above, we show that PlasticityNet with learned plasticity return mapping under neoHookean elasticity can generate qualitatively similar results (Figure 8) to those from PlasticityNetwith closed-form return mapping under the StVK elasticity.
5.3 An Ablation Study on Stability Regularizer
[Figure]
As an ablation study for the stability regularizer in Equation 11, we compare the simulations with and without theregularizer on two 2D examples (Figure 10).
Without theregularizer, the metal frame can not even stay in its original rest configuration after the first time step.
In the sand example, particles in the highlighted regions tend to separate from the sand column in a non-physicalmanner.
These demonstrate that our regularizer significantly improves the stability of the simulation.
5.4 Testing on 3D Simulations
Extending PlasticityNet to support 3D simulation is straightforward.
We only need to increase thedimension of the inputs to the PlasticityNet.
To improve the expressiveness of the network, we alsoincrease the dimension of hidden variables.
Here we demonstrate the 3D versions of our 2D exampleswith similar physical parameters: 3D sand plasticity (Figure 11), 3D snow plasticity (Figure 12), and3D metal plasticity (Figure 13).
The 3D metal is simulated with ∆ t = 10 − 2 , and for sand and snow,we use ∆ t = 10 − 3 s to satisfy the CFL condition [8] in MPM, preventing the particles from travelingfarther than the grid cell spacing in a single time step.
Conclusion
We proposed PlasticityNet, a neural network-based elastoplastic model learning framework that isagnostic to spatial discretizations.
PlasticityNet represents the elastoplastic forces as the positionalgradients of learned potential energies, so that optimization time integration could be applied toachieve robust and efficient simulation at large time step sizes.
We demonstrated that low-levelcomponents in traditional physical simulation frameworks can be substituted with neural networksto obtain desired numerical properties that benefit the computation.
Notably, this also avoidstedious analytical derivations or expensive nonlinear root-findings without significantly sacrificing theaccuracy.
We believe our work can inspire more research that applies machine learning to physicalsimulation in the bottom-up style, maintaining fundamental physical properties and applicability togeneral scenarios.
Limitations and Future Work There are several limitations of our framework.
First, we can notguarantee our fixed-point iteration procedure will converge for arbitrary scenes.
It is theoreticallyvaluable to explore under what conditions the fixed-point can converge and what kinds of trainingloss functions can accelerate the convergence.
Second, although the regularizer added during thesimulation improves the stability of the simulation without changing the solution at convergence, itmay introduce some artificial viscosity because the regularized energy penalizes deformations awayfrom F 0 .
Running more fixed-point iterations can alleviate this issue.
It will also be interesting to explore adaptive weighting mechanisms for the regularizer.
Third, we do not consider the Hessian ofthe learned plastic energy in our training.
Since we use second-order methods to perform optimizationtime integration, the properties of the Hessian matrices may have an impact on the convergence ofthe optimization method.
Although the Jacobian matrices of the target gradients are asymmetric, itmay be helpful if the Hessian of our learned elastoplastic energy can approximate them so that thestiffness of the material can be more accurately resolved.
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A Appendix
A.1 Network Architecture
All our models are using the Multilayer Perceptron (MLP) architecture with Swiss activation functions( x sigmoid( x ) ) except the output layer.
They are trained using ADAM optimizer with the sameparameters: initial learning rate α = 0 .
01 , decay rate γ = 0 .
95 , decay step 1000 .
The datasetis generated during the training process with random sampling, as discussed in Section 5.1, andthe batch size is 2 16 for all cases.
The models are all trained with 20000 epochs.
The detailedarchitectures for each model is listed in Table 1.
A.2 Technical Details on Plasticity Models
We focus on isotropic materials, where the elasticity and plasticity can both be described in thediagonal space without loss of generality.
Given the polar singular value decomposition of the
[Table]
deformation gradient F = U Diag( Σ ) V ⊤ , the Kirchhoff stress τ and the return mapping Z can bothbe computed solely by Σ as τ Σ and Z Σ , and then restored to the full space via τ =
U Diag( τ Σ ) V ⊤and Z
=
U
Diag( Z Σ ) V ⊤ .
In the following sections, we will omit the superscript Σ and only discussthe models in the diagonal space.
Here is a list of the material parameters that will be mentioned in the following sections:
Notation
Meaning
Relation to ( E, ν )
E
Young’s modulus / ν
Poisson’s ratio / µ
Shear modulus
[Equation]
[Equation]
Lamé modulus
[Equation]
K
Bulk modulus
[Equation]
ν )
A.2.1 Sand Plasticity
We use StVK elasticity and Drucker-Prager plasticity for sand simulations [20].
The Kirchhoff stress τ of StVK elasticity is defined as
[Equation]
The elastic region is characterised in the stress space as:
[Equation]
where α = (cid:113) 2 3 2sin ϕ f 3 − sin ϕ f and ϕ f is the friction angle.
In our sandexamples, ϕ f is set to π 6 .
The elastic region in the stress space isshown in Figure 14.
The return mapping for the Drucker-Prager plasticity is
[Equation]
where δγ = ∥ ˆ ϵ ∥
+ α ( dλ +2 µ )sum( ϵ ) 2 µ .
A.2.2 Snow Plasticity
We use neo-Hookean elasticity and non-associative Cam-Clay(NACC) plasticity for snow simulations [50].
The Kirchhoff stress of neo-Hookean elasticity is
[Equation] [Equation]
The elastic region of NACC is characterized by
[Equation]
where
[Equation]
ξ, β, ϕ f are the parameters of plasticity and α is the hardening state.
The elastic region in the stress space is shown in Figure 15.
In oursnow examples, ξ = 0 .
5 , β = 0 .
3 and ϕ f = π 4 .
The return mapping is defined as
[Equation]
Otherwise (20)
Please refer to [50] for the hardening state update procedure, which is controlled by the simulator.
For PlasticityNet, we set h = min { α, 0 } as the hardening state input.
During the training, we sampleh ∈ [ − 0 .
5 , 0] for 2D snow and h ∈ [ − 1 , 0] for 3D snow.
A.2.3 Metal Plasticity under StVK Elasticity
We use StVK elasticity and von-Mises plasticity for metal simulations [20].
This combinationprovides a closed-form return mapping projection.
The elastic region is characterized by
[Equation]
where τ y controls the radius of the yield surface in the stress space(Figure 16).
The return mapping for the von-Mises plasticity is defined as
[Equation]
where δγ = ∥ ˆ ϵ ∥ F − τ y 2 µ .
Under hardening, τ Y is updated with
[Equation]
where ξ is the hardening coefficient.
We use h
= τ Y 2 µ as the hardening state input to our PlasticityNet.
During the training, h is sampled from [0 , 1] .
A.2.4 Metal Plasticity under Neo-Hookean Elasticity
The combination of neo-Hookean elasticity and von-Mises plasticity does not have a closed-formreturn mapping, we thereby use this combination for the task of learning metal plasticity returnmapping.
The Kirchhoff stress of neo-Hookean elasticity is given by
[Equation]
The implicit representation of the elastic region we used in the training of the return mapping is givenby
[Equation]
During training, h is sampled from [0 , 1] .