96b196de58
This commit introduces the GNN-based surrogate for Zernike mirror optimization and the M1 mirror study progression from V12 (GNN validation) to V13 (pure NSGA-II). ## GNN Surrogate Module (optimization_engine/gnn/) New module for Graph Neural Network surrogate prediction of mirror deformations: - `polar_graph.py`: PolarMirrorGraph - fixed 3000-node polar grid structure - `zernike_gnn.py`: ZernikeGNN with design-conditioned message passing - `differentiable_zernike.py`: GPU-accelerated Zernike fitting and objectives - `train_zernike_gnn.py`: ZernikeGNNTrainer with multi-task loss - `gnn_optimizer.py`: ZernikeGNNOptimizer for turbo mode (~900k trials/hour) - `extract_displacement_field.py`: OP2 to HDF5 field extraction - `backfill_field_data.py`: Extract fields from existing FEA trials Key innovation: Design-conditioned convolutions that modulate message passing based on structural design parameters, enabling accurate field prediction. ## M1 Mirror Studies ### V12: GNN Field Prediction + FEA Validation - Zernike GNN trained on V10/V11 FEA data (238 samples) - Turbo mode: 5000 GNN predictions → top candidates → FEA validation - Calibration workflow for GNN-to-FEA error correction - Scripts: run_gnn_turbo.py, validate_gnn_best.py, compute_full_calibration.py ### V13: Pure NSGA-II FEA (Ground Truth) - Seeds 217 FEA trials from V11+V12 - Pure multi-objective NSGA-II without any surrogate - Establishes ground-truth Pareto front for GNN accuracy evaluation - Narrowed blank_backface_angle range to [4.0, 5.0] ## Documentation Updates - SYS_14: Added Zernike GNN section with architecture diagrams - CLAUDE.md: Added GNN module reference and quick start - V13 README: Study documentation with seeding strategy 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
109 lines
4.1 KiB
Python
109 lines
4.1 KiB
Python
"""Test PolarMirrorGraph with actual V11 data."""
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import sys
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sys.path.insert(0, "C:/Users/Antoine/Atomizer")
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import numpy as np
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from pathlib import Path
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from optimization_engine.gnn.polar_graph import PolarMirrorGraph, create_mirror_dataset
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from optimization_engine.gnn.extract_displacement_field import load_field
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# Test 1: Basic graph construction
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print("="*60)
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print("TEST 1: Graph Construction")
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print("="*60)
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graph = PolarMirrorGraph(r_inner=100, r_outer=650, n_radial=50, n_angular=60)
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print(f"\n{graph}")
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node_feat = graph.get_node_features(normalized=True)
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edge_feat = graph.get_edge_features(normalized=True)
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print(f"\nNode features: {node_feat.shape}")
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print(f" r normalized: [{node_feat[:, 0].min():.3f}, {node_feat[:, 0].max():.3f}]")
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print(f" theta normalized: [{node_feat[:, 1].min():.3f}, {node_feat[:, 1].max():.3f}]")
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print(f" x normalized: [{node_feat[:, 2].min():.3f}, {node_feat[:, 2].max():.3f}]")
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print(f" y normalized: [{node_feat[:, 3].min():.3f}, {node_feat[:, 3].max():.3f}]")
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print(f"\nEdge features: {edge_feat.shape}")
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print(f" Edges per node: {edge_feat.shape[0] / graph.n_nodes:.1f}")
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# Test 2: Load actual V11 field data and interpolate
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print("\n" + "="*60)
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print("TEST 2: Interpolation from V11 Data")
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print("="*60)
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field_path = Path("C:/Users/Antoine/Atomizer/studies/m1_mirror_adaptive_V11/gnn_data/trial_0091/displacement_field.h5")
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if field_path.exists():
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field_data = load_field(field_path)
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print(f"\nLoaded field data:")
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print(f" FEA nodes: {len(field_data['node_ids'])}")
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print(f" Subcases: {list(field_data['z_displacement'].keys())}")
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# Interpolate to polar grid
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result = graph.interpolate_field_data(field_data, subcases=[1, 2, 3, 4])
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z_grid = result['z_displacement']
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print(f"\nInterpolation result:")
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print(f" Shape: {z_grid.shape} (expected: {graph.n_nodes} × 4)")
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print(f" NaN count: {np.sum(np.isnan(z_grid))}")
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for i, sc in enumerate([1, 2, 3, 4]):
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disp = z_grid[:, i]
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print(f" Subcase {sc}: [{disp.min():.6f}, {disp.max():.6f}] mm")
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# Test relative deformation computation
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print("\n--- Relative Deformations (like Zernike extraction) ---")
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disp_90 = z_grid[:, 0] # Subcase 1 = 90°
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disp_20 = z_grid[:, 1] # Subcase 2 = 20° (reference)
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disp_40 = z_grid[:, 2] # Subcase 3 = 40°
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disp_60 = z_grid[:, 3] # Subcase 4 = 60°
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rel_40_vs_20 = disp_40 - disp_20
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rel_60_vs_20 = disp_60 - disp_20
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rel_90_vs_20 = disp_90 - disp_20
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print(f" 40° - 20°: [{rel_40_vs_20.min():.6f}, {rel_40_vs_20.max():.6f}] mm, RMS={np.std(rel_40_vs_20)*1e6:.2f} nm")
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print(f" 60° - 20°: [{rel_60_vs_20.min():.6f}, {rel_60_vs_20.max():.6f}] mm, RMS={np.std(rel_60_vs_20)*1e6:.2f} nm")
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print(f" 90° - 20°: [{rel_90_vs_20.min():.6f}, {rel_90_vs_20.max():.6f}] mm, RMS={np.std(rel_90_vs_20)*1e6:.2f} nm")
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else:
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print(f"Field file not found: {field_path}")
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# Test 3: Create full dataset from V11
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print("\n" + "="*60)
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print("TEST 3: Create Dataset from V11")
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print("="*60)
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study_dir = Path("C:/Users/Antoine/Atomizer/studies/m1_mirror_adaptive_V11")
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if (study_dir / "gnn_data").exists():
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dataset = create_mirror_dataset(study_dir, polar_graph=graph, verbose=True)
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print(f"\n--- Dataset Summary ---")
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print(f"Total samples: {len(dataset)}")
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if dataset:
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# Check consistency
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shapes = [d['z_displacement'].shape for d in dataset]
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unique_shapes = set(shapes)
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print(f"Unique shapes: {unique_shapes}")
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# Design variable info
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n_vars = len(dataset[0]['design_vars'])
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print(f"Design variables: {n_vars}")
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if dataset[0]['design_names']:
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print(f" Names: {dataset[0]['design_names'][:3]}...")
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# Stack for statistics
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all_z = np.stack([d['z_displacement'] for d in dataset])
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print(f"\nAll data shape: {all_z.shape}")
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print(f" Per-subcase ranges:")
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for i in range(4):
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print(f" Subcase {i+1}: [{all_z[:,:,i].min():.6f}, {all_z[:,:,i].max():.6f}] mm")
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else:
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print(f"No gnn_data folder found in {study_dir}")
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print("\n" + "="*60)
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print("✓ All tests completed!")
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print("="*60)
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