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>
38 lines
1.3 KiB
Python
38 lines
1.3 KiB
Python
"""Quick test script for displacement field extraction."""
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import h5py
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import numpy as np
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from pathlib import Path
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# Test file
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h5_path = Path("C:/Users/Antoine/Atomizer/studies/m1_mirror_adaptive_V11/gnn_data/trial_0091/displacement_field.h5")
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print(f"Testing: {h5_path}")
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print(f"Exists: {h5_path.exists()}")
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if h5_path.exists():
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with h5py.File(h5_path, 'r') as f:
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print(f"\nDatasets in file: {list(f.keys())}")
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node_coords = f['node_coords'][:]
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node_ids = f['node_ids'][:]
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print(f"\nTotal nodes: {len(node_ids)}")
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# Calculate radial position
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r = np.sqrt(node_coords[:, 0]**2 + node_coords[:, 1]**2)
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print(f"Radial range: [{r.min():.1f}, {r.max():.1f}] mm")
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print(f"Z range: [{node_coords[:, 2].min():.1f}, {node_coords[:, 2].max():.1f}] mm")
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# Check nodes in optical surface range (100-650 mm radius)
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surface_mask = (r >= 100) & (r <= 650)
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print(f"Nodes in r=[100, 650]: {np.sum(surface_mask)}")
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# Check subcases
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subcases = [k for k in f.keys() if k.startswith("subcase_")]
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print(f"Subcases: {subcases}")
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if subcases:
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for sc in subcases:
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disp = f[sc][:]
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print(f" {sc}: Z-disp range [{disp.min():.4f}, {disp.max():.4f}] mm")
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