Upload examples/multi_fidelity_example.py with huggingface_hub

examples/multi_fidelity_example.py

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+"""
+Example: Multi-Fidelity Bayesian Optimization
+
+Uses the physics model as a cheap low-fidelity source and experimental
+measurements as the expensive high-fidelity source. The multi-fidelity GP
+learns the correlation between fidelities to transfer knowledge.
+"""
+
+import torch
+from torch import Tensor
+
+from physics_informed_bo.experiment.parameter_space import ParameterSpace
+from physics_informed_bo.models.multi_fidelity import MultiFidelitySurrogate
+
+
+def physics_model(X: Tensor) -> Tensor:
+    """Low-fidelity physics model."""
+    x1, x2 = X[:, 0], X[:, 1]
+    return torch.sin(x1) * x2 + x1 * 0.5
+
+
+def true_function(X: Tensor) -> Tensor:
+    """High-fidelity ground truth (simulating experiments)."""
+    x1, x2 = X[:, 0], X[:, 1]
+    return torch.sin(x1) * x2 + x1 * 0.5 + 0.3 * torch.cos(3 * x1 * x2)
+
+
+def main():
+    torch.manual_seed(42)
+
+    # Define space
+    space = ParameterSpace()
+    space.add_continuous("x1", 0.0, 6.28)
+    space.add_continuous("x2", 0.0, 5.0)
+
+    # Multi-fidelity surrogate
+    mf_model = MultiFidelitySurrogate(
+        physics_fn=physics_model,
+        device="cpu",
+    )
+
+    # Small set of expensive experiments
+    X_exp = torch.tensor([
+        [1.0, 2.0], [3.0, 1.0], [5.0, 4.0], [2.0, 3.0], [4.0, 2.5]
+    ], dtype=torch.float64)
+    y_exp = true_function(X_exp).unsqueeze(-1) + 0.05 * torch.randn(5, 1, dtype=torch.float64)
+
+    # Build multi-fidelity dataset (physics=low, experiments=high)
+    X_mf, y_mf = mf_model.build_multi_fidelity_data(
+        X_experiment=X_exp,
+        y_experiment=y_exp,
+        n_physics_points=50,
+    )
+
+    print(f"Multi-fidelity dataset: {len(X_mf)} points "
+          f"({len(X_mf) - len(X_exp)} physics + {len(X_exp)} experimental)")
+
+    # Fit the model
+    mf_model.fit(X_mf, y_mf)
+
+    # Predict at new points (always at high fidelity)
+    X_test = torch.tensor([[2.5, 2.5], [4.0, 3.0]], dtype=torch.float64)
+    mean, var = mf_model.predict(X_test)
+
+    print("\nPredictions (high fidelity):")
+    for i, (m, v) in enumerate(zip(mean, var)):
+        true_val = true_function(X_test[i:i+1]).item()
+        print(f"  x={X_test[i].tolist()} -> pred={m.item():.3f} ± {v.sqrt().item():.3f} "
+              f"(true={true_val:.3f})")
+
+
+if __name__ == "__main__":
+    main()