feat: Add Zernike GNN surrogate module and M1 mirror V12/V13 studies
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>
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Antoine
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studies/m1_mirror_adaptive_V12/validate_gnn_best.py
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studies/m1_mirror_adaptive_V12/validate_gnn_best.py
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#!/usr/bin/env python3
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"""
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Validate GNN Best Designs with FEA
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===================================
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Reads best designs from gnn_turbo_results.json and validates with actual FEA.
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Usage:
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python validate_gnn_best.py # Full validation (solve + extract)
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python validate_gnn_best.py --resume # Resume: skip existing OP2, just extract Zernike
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"""
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import sys
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import json
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import argparse
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).parent.parent.parent))
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from optimization_engine.gnn.gnn_optimizer import ZernikeGNNOptimizer, GNNPrediction
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from optimization_engine.extractors import ZernikeExtractor
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# Paths
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STUDY_DIR = Path(__file__).parent
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RESULTS_FILE = STUDY_DIR / "gnn_turbo_results.json"
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CONFIG_PATH = STUDY_DIR / "1_setup" / "optimization_config.json"
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CHECKPOINT_PATH = Path("C:/Users/Antoine/Atomizer/zernike_gnn_checkpoint.pt")
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def extract_from_existing_op2(study_dir: Path, turbo_results: dict, config: dict) -> list:
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"""Extract Zernike from existing OP2 files in iter9000-9002."""
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import numpy as np
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iterations_dir = study_dir / "2_iterations"
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zernike_settings = config.get('zernike_settings', {})
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results = []
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design_keys = ['best_40_vs_20', 'best_60_vs_20', 'best_mfg_90']
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for i, key in enumerate(design_keys):
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trial_num = 9000 + i
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iter_dir = iterations_dir / f"iter{trial_num}"
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print(f"\n[{i+1}/3] Processing {iter_dir.name} ({key})")
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# Find OP2 file
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op2_files = list(iter_dir.glob("*-solution_1.op2"))
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if not op2_files:
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print(f" ERROR: No OP2 file found")
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results.append({
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'design': turbo_results[key]['design_vars'],
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'gnn_objectives': turbo_results[key]['objectives'],
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'fea_objectives': None,
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'status': 'no_op2',
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'trial_num': trial_num
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})
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continue
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op2_path = op2_files[0]
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size_mb = op2_path.stat().st_size / 1e6
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print(f" OP2: {op2_path.name} ({size_mb:.1f} MB)")
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if size_mb < 50:
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print(f" ERROR: OP2 too small, likely incomplete")
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results.append({
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'design': turbo_results[key]['design_vars'],
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'gnn_objectives': turbo_results[key]['objectives'],
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'fea_objectives': None,
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'status': 'incomplete_op2',
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'trial_num': trial_num
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})
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continue
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# Extract Zernike
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try:
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extractor = ZernikeExtractor(
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str(op2_path),
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bdf_path=None,
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displacement_unit=zernike_settings.get('displacement_unit', 'mm'),
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n_modes=zernike_settings.get('n_modes', 50),
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filter_orders=zernike_settings.get('filter_low_orders', 4)
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)
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ref = zernike_settings.get('reference_subcase', '2')
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# Extract objectives: 40 vs 20, 60 vs 20, mfg 90
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rel_40 = extractor.extract_relative("3", ref)
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rel_60 = extractor.extract_relative("4", ref)
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rel_90 = extractor.extract_relative("1", ref)
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fea_objectives = {
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'rel_filtered_rms_40_vs_20': rel_40['relative_filtered_rms_nm'],
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'rel_filtered_rms_60_vs_20': rel_60['relative_filtered_rms_nm'],
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'mfg_90_optician_workload': rel_90['relative_rms_filter_j1to3'],
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}
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# Compute errors
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gnn_obj = turbo_results[key]['objectives']
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errors = {}
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for obj_name in ['rel_filtered_rms_40_vs_20', 'rel_filtered_rms_60_vs_20', 'mfg_90_optician_workload']:
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gnn_val = gnn_obj[obj_name]
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fea_val = fea_objectives[obj_name]
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errors[f'{obj_name}_abs_error'] = abs(gnn_val - fea_val)
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errors[f'{obj_name}_pct_error'] = 100 * abs(gnn_val - fea_val) / max(fea_val, 0.01)
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print(f" FEA: 40vs20={fea_objectives['rel_filtered_rms_40_vs_20']:.2f} nm "
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f"(GNN: {gnn_obj['rel_filtered_rms_40_vs_20']:.2f}, err: {errors['rel_filtered_rms_40_vs_20_pct_error']:.1f}%)")
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print(f" 60vs20={fea_objectives['rel_filtered_rms_60_vs_20']:.2f} nm "
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f"(GNN: {gnn_obj['rel_filtered_rms_60_vs_20']:.2f}, err: {errors['rel_filtered_rms_60_vs_20_pct_error']:.1f}%)")
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print(f" mfg90={fea_objectives['mfg_90_optician_workload']:.2f} nm "
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f"(GNN: {gnn_obj['mfg_90_optician_workload']:.2f}, err: {errors['mfg_90_optician_workload_pct_error']:.1f}%)")
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results.append({
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'design': turbo_results[key]['design_vars'],
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'gnn_objectives': gnn_obj,
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'fea_objectives': fea_objectives,
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'errors': errors,
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'trial_num': trial_num,
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'status': 'success'
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})
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except Exception as e:
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print(f" ERROR extracting Zernike: {e}")
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results.append({
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'design': turbo_results[key]['design_vars'],
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'gnn_objectives': turbo_results[key]['objectives'],
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'fea_objectives': None,
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'status': 'extraction_error',
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'error': str(e),
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'trial_num': trial_num
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})
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return results
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def main():
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parser = argparse.ArgumentParser(description='Validate GNN predictions with FEA')
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parser.add_argument('--resume', action='store_true',
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help='Resume: extract Zernike from existing OP2 files instead of re-solving')
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args = parser.parse_args()
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# Load GNN turbo results
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print("Loading GNN turbo results...")
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with open(RESULTS_FILE) as f:
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turbo_results = json.load(f)
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# Load config
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with open(CONFIG_PATH) as f:
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config = json.load(f)
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# Show candidates
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candidates = []
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for key in ['best_40_vs_20', 'best_60_vs_20', 'best_mfg_90']:
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data = turbo_results[key]
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pred = GNNPrediction(
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design_vars=data['design_vars'],
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objectives={k: float(v) for k, v in data['objectives'].items()}
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)
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candidates.append(pred)
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print(f"\n{key}:")
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print(f" 40vs20: {pred.objectives['rel_filtered_rms_40_vs_20']:.2f} nm")
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print(f" 60vs20: {pred.objectives['rel_filtered_rms_60_vs_20']:.2f} nm")
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print(f" mfg90: {pred.objectives['mfg_90_optician_workload']:.2f} nm")
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if args.resume:
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# Resume mode: extract from existing OP2 files
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print("\n" + "="*60)
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print("RESUME MODE: Extracting Zernike from existing OP2 files")
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print("="*60)
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validation_results = extract_from_existing_op2(STUDY_DIR, turbo_results, config)
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else:
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# Full mode: run FEA + extract
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print("\n" + "="*60)
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print("LOADING GNN OPTIMIZER FOR FEA VALIDATION")
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print("="*60)
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optimizer = ZernikeGNNOptimizer.from_checkpoint(CHECKPOINT_PATH, CONFIG_PATH)
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print(f"Design variables: {len(optimizer.design_names)}")
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print("\n" + "="*60)
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print("RUNNING FEA VALIDATION")
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print("="*60)
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validation_results = optimizer.validate_with_fea(
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candidates=candidates,
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study_dir=STUDY_DIR,
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verbose=True,
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start_trial_num=9000
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)
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# Summary
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import numpy as np
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successful = [r for r in validation_results if r['status'] == 'success']
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print(f"\n{'='*60}")
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print(f"VALIDATION SUMMARY")
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print(f"{'='*60}")
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print(f"Successful: {len(successful)}/{len(validation_results)}")
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if successful:
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avg_errors = {}
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for obj in ['rel_filtered_rms_40_vs_20', 'rel_filtered_rms_60_vs_20', 'mfg_90_optician_workload']:
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avg_errors[obj] = np.mean([r['errors'][f'{obj}_pct_error'] for r in successful])
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print(f"\nAverage GNN prediction errors:")
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print(f" 40 vs 20: {avg_errors['rel_filtered_rms_40_vs_20']:.1f}%")
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print(f" 60 vs 20: {avg_errors['rel_filtered_rms_60_vs_20']:.1f}%")
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print(f" mfg 90: {avg_errors['mfg_90_optician_workload']:.1f}%")
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# Save validation report
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from datetime import datetime
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output_path = STUDY_DIR / "gnn_validation_report.json"
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report = {
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'timestamp': datetime.now().isoformat(),
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'mode': 'resume' if args.resume else 'full',
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'n_candidates': len(validation_results),
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'n_successful': len(successful),
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'results': validation_results,
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}
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if successful:
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report['error_summary'] = {
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obj: {
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'mean_pct': float(np.mean([r['errors'][f'{obj}_pct_error'] for r in successful])),
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'std_pct': float(np.std([r['errors'][f'{obj}_pct_error'] for r in successful])),
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'max_pct': float(np.max([r['errors'][f'{obj}_pct_error'] for r in successful])),
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}
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for obj in ['rel_filtered_rms_40_vs_20', 'rel_filtered_rms_60_vs_20', 'mfg_90_optician_workload']
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}
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with open(output_path, 'w') as f:
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json.dump(report, f, indent=2)
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print(f"\nValidation report saved to: {output_path}")
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print("\nDone!")
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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