feat: Add M1 mirror Zernike optimization with correct RMS calculation

Major improvements to telescope mirror optimization workflow: Assembly FEM Workflow (solve_simulation.py): - Fixed multi-part assembly FEM update sequence - Use ImportFromFile() for reliable expression updates - Add DuplicateNodesCheckBuilder with MergeOccurrenceNodes=True - Switch to Foreground solve mode for multi-subcase solutions - Add detailed logging and diagnostics for node merge operations Zernike RMS Calculation: - CRITICAL FIX: Use correct surface-based RMS formula - Global RMS = sqrt(mean(W^2)) from actual WFE values - Filtered RMS = sqrt(mean(W_residual^2)) after removing low-order fit - This matches zernike_Post_Script_NX.py (optical standard) - Previous WRONG formula was: sqrt(sum(coeffs^2)) - Add compute_rms_filter_j1to3() for optician workload metric Subcase Mapping: - Fix subcase mapping to match NX model: - Subcase 1 = 90 deg (polishing orientation) - Subcase 2 = 20 deg (reference) - Subcase 3 = 40 deg - Subcase 4 = 60 deg New Study: M1 Mirror Zernike Optimization - Full optimization config with 11 design variables - 3 objectives: rel_filtered_rms_40_vs_20, rel_filtered_rms_60_vs_20, mfg_90_optician_workload - Neural surrogate support for accelerated optimization Documentation: - Update ZERNIKE_INTEGRATION.md with correct RMS formula - Update ASSEMBLY_FEM_WORKFLOW.md with expression import and node merge details - Add reference scripts from original zernike_Post_Script_NX.py 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-28 16:30:15 -05:00
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+# M1 Mirror Zernike Optimization Dashboard
+
+## Study Overview
+
+**Objective**: Optimize telescope primary mirror (M1) support structure to minimize wavefront error across different gravity orientations.
+
+**Method**: Hybrid FEA + Neural Network acceleration using Zernike polynomial decomposition.
+
+## Quick Start
+
+### 1. Prepare Model Files
+
+Copy your NX model files to:
+```
+studies/m1_mirror_zernike_optimization/1_setup/model/
+```
+
+Required files:
+- `ASSY_M1.prt` (or your assembly name)
+- `ASSY_M1_assyfem1.afm`
+- `ASSY_M1_assyfem1_sim1.sim`
+- Associated `.fem` and `_i.prt` files
+
+### 2. Run FEA Trials (Build Training Data)
+
+```bash
+cd studies/m1_mirror_zernike_optimization
+python run_optimization.py --run --trials 40
+```
+
+This will:
+- Run ~40 FEA trials (10-15 min each = ~8-10 hours)
+- Extract 50 Zernike coefficients for each subcase (20/40/60/90 deg)
+- Store all data in Optuna database
+
+### 3. Train Neural Surrogate
+
+```bash
+python run_optimization.py --train-surrogate
+```
+
+Trains MLP to predict 200 outputs (50 coefficients x 4 subcases) from design variables.
+
+### 4. Run Neural-Accelerated Optimization
+
+```bash
+python run_optimization.py --run --trials 1000 --enable-nn
+```
+
+1000 trials in ~seconds!
+
+### 5. View Results
+
+**Optuna Dashboard:**
+```bash
+optuna-dashboard sqlite:///2_results/study.db --port 8081
+```
+Open http://localhost:8081
+
+## Design Variables
+
+| Variable | Range | Baseline | Units | Status |
+|----------|-------|----------|-------|--------|
+| whiffle_min | 35-55 | 40.55 | mm | **Enabled** |
+| whiffle_outer_to_vertical | 68-80 | 75.67 | deg | **Enabled** |
+| inner_circular_rib_dia | 480-620 | 534.00 | mm | **Enabled** |
+| whiffle_triangle_closeness | 50-65 | 60.00 | mm | Disabled |
+| blank_backface_angle | 3.5-5.0 | 4.23 | deg | Disabled |
+| lateral_inner_angle | 25-28.5 | 26.79 | deg | Disabled |
+| lateral_outer_angle | 13-17 | 14.64 | deg | Disabled |
+| lateral_outer_pivot | 9-12 | 10.40 | mm | Disabled |
+| lateral_inner_pivot | 9-12 | 10.07 | mm | Disabled |
+| lateral_middle_pivot | 18-23 | 20.73 | mm | Disabled |
+| lateral_closeness | 9.5-12.5 | 11.02 | mm | Disabled |
+
+Edit `1_setup/optimization_config.json` to enable/disable variables.
+
+## Objectives
+
+| Objective | Weight | Target | Description |
+|-----------|--------|--------|-------------|
+| rel_filtered_rms_40_vs_20 | 5 | 4 nm | WFE at 40° relative to 20° reference |
+| rel_filtered_rms_60_vs_20 | 5 | 10 nm | WFE at 60° relative to 20° reference |
+| mfg_90_optician_workload | 1 | 20 nm | Polishing workload at 90° orientation |
+
+**Strategy**: Weighted sum minimization (normalized by targets)
+
+## Neural Surrogate Architecture
+
+- **Input**: Design variables (3-11 depending on enabled)
+- **Output**: 200 values (50 Zernike coefficients × 4 subcases)
+- **Architecture**: MLP with 4 layers, 128 hidden units
+- **Training**: ~40 FEA samples, 200 epochs
+
+## File Structure
+
+```
+m1_mirror_zernike_optimization/
+├── 1_setup/
+│   ├── optimization_config.json    # Configuration
+│   └── model/                      # NX model files (add yours here)
+├── 2_results/
+│   ├── study.db                    # Optuna database
+│   └── zernike_surrogate/          # Trained neural model
+│       └── checkpoint_best.pt
+├── run_optimization.py             # Main script
+└── DASHBOARD.md                    # This file
+```
+
+## Commands Reference
+
+```bash
+# Run FEA optimization
+python run_optimization.py --run --trials 40
+
+# Train neural surrogate
+python run_optimization.py --train-surrogate
+
+# Run with neural acceleration
+python run_optimization.py --run --trials 1000 --enable-nn
+
+# Check status
+python run_optimization.py --status
+
+# Launch Optuna dashboard
+optuna-dashboard sqlite:///2_results/study.db --port 8081
+```
+
+## Tips
+
+1. **Start small**: Run 5-10 FEA trials first to verify everything works
+2. **Check Zernike extraction**: Verify OP2 has correct subcases (20/40/60/90)
+3. **Enable variables gradually**: Start with 3, add more after initial exploration
+4. **Neural validation**: After finding good neural designs, verify top candidates with FEA