Upload examples/minimal_example.py with huggingface_hub

examples/minimal_example.py

@@ -0,0 +1,44 @@
+"""
+Minimal example: Physics-Informed Bayesian Optimization in ~30 lines.
+
+Demonstrates the core workflow:
+1. Define a physics model
+2. Define the parameter space
+3. Create a designer and suggest experiments
+"""
+
+import torch
+from physics_informed_bo.experiment.parameter_space import ParameterSpace
+from physics_informed_bo.experiment.designer import ExperimentDesigner
+
+
+# Physics model: simple Arrhenius-like kinetics
+def physics_model(X):
+    temp, conc = X[:, 0], X[:, 1]
+    return torch.exp(-5000 / temp) * conc**0.5
+
+
+# Parameter space
+space = ParameterSpace()
+space.add_continuous("temperature", 300, 600, units="K")
+space.add_continuous("concentration", 0.1, 10.0, units="mol/L")
+
+# Some initial observations
+X_init = torch.tensor([[350.0, 1.0], [450.0, 5.0], [500.0, 3.0]])
+y_init = torch.tensor([0.1, 0.8, 0.6])
+
+# Create designer with physics + data
+designer = ExperimentDesigner(
+    parameter_space=space,
+    physics_fn=physics_model,
+    initial_data=(X_init, y_init),
+)
+
+# Suggest next experiments
+next_experiments = designer.suggest(n=3)
+print("Suggested experiments:")
+for exp in space.to_dict(next_experiments):
+    print(f"  Temperature={exp['temperature']:.1f} K, Concentration={exp['concentration']:.2f} mol/L")
+
+# After running experiments, update the designer
+# designer.update(X_new, y_new)