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Atomizer/studies/m1_mirror_adaptive_V11/README.md

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feat: Add Protocol 13 adaptive optimization, Plotly charts, and dashboard improvements ## Protocol 13: Adaptive Multi-Objective Optimization - Iterative FEA + Neural Network surrogate workflow - Initial FEA sampling, NN training, NN-accelerated search - FEA validation of top NN predictions, retraining loop - adaptive_state.json tracks iteration history and best values - M1 mirror study (V11) with 103 FEA, 3000 NN trials ## Dashboard Visualization Enhancements - Added Plotly.js interactive charts (parallel coords, Pareto, convergence) - Lazy loading with React.lazy() for performance - Code splitting: plotly.js-basic-dist (~1MB vs 3.5MB) - Chart library toggle (Recharts default, Plotly on-demand) - ExpandableChart component for full-screen modal views - ConsoleOutput component for real-time log viewing ## Documentation - Protocol 13 detailed documentation - Dashboard visualization guide - Plotly components README - Updated run-optimization skill with Mode 5 (adaptive) ## Bug Fixes - Fixed TypeScript errors in dashboard components - Fixed Card component to accept ReactNode title - Removed unused imports across components 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-12-04 07:41:54 -05:00
# M1 Mirror Adaptive Surrogate Optimization V11
Adaptive neural surrogate optimization with real FEA validation for the M1 telescope mirror support system.
**Created**: 2025-12-03
**Protocol**: Adaptive Surrogate (TPE + FEA Validation Loop)
**Status**: Ready to Run
---
## 1. Engineering Problem
### 1.1 Objective
Minimize gravitational wavefront error (WFE) deformations in a 1.2m telescope primary mirror across operational angles (20-60 deg elevation) and manufacturing orientation (90 deg polishing).
### 1.2 Physical System
- **Component**: M1 Primary Mirror Assembly with whiffle-tree support
- **Material**: Zerodur glass ceramic (E=91 GPa, CTE~0)
- **Loading**: Self-weight gravity at multiple elevation angles
- **Boundary Conditions**: 18-point whiffle-tree axial support, 3-point lateral support
- **Analysis Type**: Linear static (Nastran SOL 101) with 4 subcases (20, 40, 60, 90 deg)
### 1.3 Optical Workflow
```
Reference: 20 deg (interferometer baseline)
Operational Tracking:
- 40 deg - 20 deg = tracking deformation at 40 deg
- 60 deg - 20 deg = tracking deformation at 60 deg
Manufacturing:
- 90 deg - 20 deg = polishing correction needed
```
---
## 2. Mathematical Formulation
### 2.1 Objectives
| Objective | Goal | Weight | Formula | Target | Units |
|-----------|------|--------|---------|--------|-------|
| Operational 40-20 | minimize | 5.0 | $\text{RMS}_{filt}(Z_{40} - Z_{20})$ | 4.0 | nm |
| Operational 60-20 | minimize | 5.0 | $\text{RMS}_{filt}(Z_{60} - Z_{20})$ | 10.0 | nm |
| Manufacturing 90-20 | minimize | 1.0 | $\text{RMS}_{J1-J3}(Z_{90} - Z_{20})$ | 20.0 | nm |
Where:
- $Z_\theta$ = Zernike coefficients at elevation angle $\theta$
- $\text{RMS}_{filt}$ = RMS with J1-J4 (piston, tip, tilt, defocus) removed
- $\text{RMS}_{J1-J3}$ = RMS with only J1-J3 (piston, tip, tilt) removed
### 2.2 Design Variables
| Parameter | Symbol | Bounds | Units | Description |
|-----------|--------|--------|-------|-------------|
| lateral_inner_angle | $\alpha_i$ | [25.0, 28.5] | deg | Inner lateral support angle |
| lateral_outer_angle | $\alpha_o$ | [13.0, 17.0] | deg | Outer lateral support angle |
| lateral_outer_pivot | $p_o$ | [9.0, 12.0] | mm | Outer pivot position |
| lateral_inner_pivot | $p_i$ | [9.0, 12.0] | mm | Inner pivot position |
| lateral_middle_pivot | $p_m$ | [18.0, 23.0] | mm | Middle pivot position |
| lateral_closeness | $c_l$ | [9.5, 12.5] | mm | Lateral support spacing |
| whiffle_min | $w_{min}$ | [35.0, 55.0] | mm | Whiffle tree minimum |
| whiffle_outer_to_vertical | $w_{ov}$ | [68.0, 80.0] | deg | Whiffle outer to vertical |
| whiffle_triangle_closeness | $w_{tc}$ | [50.0, 65.0] | mm | Whiffle triangle spacing |
| blank_backface_angle | $\beta$ | [3.5, 5.0] | deg | Mirror blank backface angle |
| inner_circular_rib_dia | $d_{rib}$ | [480.0, 620.0] | mm | Inner rib diameter |
**Design Space**: $\mathbf{x} \in \mathbb{R}^{11}$
### 2.3 Weighted Objective
$$f(\mathbf{x}) = \frac{w_1 \cdot \frac{O_{40-20}}{t_1} + w_2 \cdot \frac{O_{60-20}}{t_2} + w_3 \cdot \frac{O_{mfg}}{t_3}}{w_1 + w_2 + w_3}$$
Where $w_i$ are weights and $t_i$ are target values for normalization.
---
## 3. Adaptive Optimization Algorithm
### 3.1 Configuration
| Parameter | Value | Description |
|-----------|-------|-------------|
| Algorithm | TPE + FEA Validation | Bayesian optimization with real validation |
| NN Trials/Iteration | 1000 | Fast surrogate exploration |
| FEA Trials/Iteration | 5 | Real validation per iteration |
| Strategy | Hybrid | 70% best + 30% uncertain (exploration) |
| Convergence | 0.3 nm threshold | Stop if no improvement |
| Patience | 5 iterations | Iterations without improvement before stopping |
### 3.2 Adaptive Loop
```
┌─────────────────────────────────────────────────────────────────────┐
│ ADAPTIVE OPTIMIZATION LOOP │
├─────────────────────────────────────────────────────────────────────┤
│ │
│ 1. LOAD V10 FEA DATA (90 trials) │
│ └─ Cross-link: ../m1_mirror_zernike_optimization_V10/ │
│ │
│ 2. TRAIN INITIAL SURROGATE │
│ └─ MLP: 11 inputs → 3 objectives │
│ └─ Architecture: [128, 256, 256, 128, 64] │
│ │
│ 3. ITERATION LOOP: │
│ │ │
│ ├─ 3a. SURROGATE EXPLORATION (1000 NN trials) │
│ │ └─ TPE sampler with MC Dropout uncertainty │
│ │ │
│ ├─ 3b. CANDIDATE SELECTION │
│ │ └─ Hybrid: best weighted + highest uncertainty │
│ │ │
│ ├─ 3c. FEA VALIDATION (5 real simulations) │
│ │ └─ NX Nastran SOL 101 with ZernikeExtractor │
│ │ └─ Tag trials: source='FEA' │
│ │ │
│ ├─ 3d. UPDATE BEST & CHECK IMPROVEMENT │
│ │ └─ Track no_improvement_count │
│ │ │
│ ├─ 3e. RETRAIN SURROGATE │
│ │ └─ Include new FEA data │
│ │ │
│ └─ 3f. CONVERGENCE CHECK │
│ └─ Stop if patience exceeded │
│ │
│ 4. SAVE FINAL RESULTS │
│ └─ Best FEA-validated design │
│ │
└─────────────────────────────────────────────────────────────────────┘
```
---
## 4. Result Extraction Methods
### 4.1 Relative Filtered RMS Extraction
| Attribute | Value |
|-----------|-------|
| **Extractor** | `ZernikeExtractor.extract_relative()` |
| **Module** | `optimization_engine.extractors.extract_zernike` |
| **Source** | `.op2` file from NX Nastran |
| **Output** | nm (nanometers) |
**Algorithm**:
1. Parse OP2 displacement results for target and reference subcases
2. Fit Zernike polynomials (Noll indexing, J1-J50)
3. Compute difference: $\Delta Z_j = Z_j^{target} - Z_j^{ref}$
4. Filter low orders (J1-J4 for operational, J1-J3 for manufacturing)
5. Compute RMS: $\text{RMS} = \sqrt{\sum_{j} \Delta Z_j^2}$
**Code**:
```python
from optimization_engine.extractors import ZernikeExtractor
extractor = ZernikeExtractor(op2_path, displacement_unit='mm', n_modes=50)
# Operational objectives
rel_40 = extractor.extract_relative("3", "2") # 40 deg vs 20 deg
obj_40_20 = rel_40['relative_filtered_rms_nm']
rel_60 = extractor.extract_relative("4", "2") # 60 deg vs 20 deg
obj_60_20 = rel_60['relative_filtered_rms_nm']
# Manufacturing objective
rel_90 = extractor.extract_relative("1", "2") # 90 deg vs 20 deg
obj_mfg = rel_90['relative_rms_filter_j1to3']
```
---
## 5. Neural Surrogate
### 5.1 Architecture
```
Input (11) → Dense(128) → BN → ReLU → Dropout(0.1)
→ Dense(256) → BN → ReLU → Dropout(0.1)
→ Dense(256) → BN → ReLU → Dropout(0.1)
→ Dense(128) → BN → ReLU → Dropout(0.1)
→ Dense(64) → BN → ReLU → Dropout(0.1)
→ Dense(3) → Output
```
### 5.2 MC Dropout Uncertainty
For candidate selection, MC Dropout provides uncertainty estimates:
- Enable dropout at inference time
- Run 30 forward passes
- Compute mean and std of predictions
- High std = high uncertainty = exploration candidate
### 5.3 Training Configuration
| Setting | Value |
|---------|-------|
| Optimizer | AdamW |
| Learning Rate | 0.001 |
| Weight Decay | 1e-4 |
| Scheduler | CosineAnnealing |
| Epochs | 300 |
| Batch Size | 16 |
---
## 6. Study File Structure
```
m1_mirror_adaptive_V11/
│
├── 1_setup/ # INPUT CONFIGURATION
│ ├── model/ # NX Model Files (copied from V10)
│ │ └── [NX files copied on first run]
│ └── optimization_config.json # Study configuration
│
├── 2_iterations/ # FEA iteration folders
│ └── iter{N}/ # Per-trial working directory
│
├── 3_results/ # OUTPUT
│ ├── study.db # Optuna database (NN trials)
│ ├── adaptive_state.json # Iteration state
│ ├── surrogate_*.pt # Model checkpoints
│ ├── final_results.json # Best results
│ └── optimization.log # Execution log
│
├── run_optimization.py # Main entry point
├── README.md # This blueprint
└── STUDY_REPORT.md # Results report (updated as runs)
```
---
## 7. Dashboard Integration
### Trial Source Differentiation
| Source | Tag | Display |
|--------|-----|---------|
| V10 FEA | `V10_FEA` | Used for training only |
| V11 FEA | `FEA` | Blue circles |
| V11 NN | `NN` | Orange crosses |
### Attributes Stored
For each trial:
- `source`: 'FEA' or 'NN'
- `predicted_40_vs_20`: NN prediction (if NN)
- `predicted_60_vs_20`: NN prediction (if NN)
- `predicted_mfg`: NN prediction (if NN)
---
## 8. Quick Start
```bash
# Navigate to study
cd studies/m1_mirror_adaptive_V11
# Start adaptive optimization
python run_optimization.py --start
# With custom settings
python run_optimization.py --start --fea-batch 3 --patience 7
# Monitor in dashboard
# FEA trials: Blue circles
# NN trials: Orange crosses
```
### Command Line Options
| Option | Default | Description |
|--------|---------|-------------|
| `--start` | - | Required to begin optimization |
| `--fea-batch` | 5 | FEA validations per iteration |
| `--nn-trials` | 1000 | NN trials per iteration |
| `--patience` | 5 | Iterations without improvement |
| `--strategy` | hybrid | best / uncertain / hybrid |
---
## 9. Configuration Reference
**File**: `1_setup/optimization_config.json`
| Section | Key | Description |
|---------|-----|-------------|
| `source_study.database` | `../m1_mirror_zernike_optimization_V10/3_results/study.db` | V10 training data |
| `objectives[]` | 3 objectives | Relative filtered RMS metrics |
| `adaptive_settings.*` | Iteration config | NN trials, FEA batch, patience |
| `surrogate_settings.*` | Neural config | Architecture, dropout, MC samples |
| `dashboard_settings.*` | Visualization | FEA/NN markers and colors |
---
## 10. References
- **Deb et al.** (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. *IEEE Trans. Evolutionary Computation*.
- **Noll, R.J.** (1976). Zernike polynomials and atmospheric turbulence. *JOSA*.
- **Gal, Y. & Ghahramani, Z.** (2016). Dropout as a Bayesian Approximation. *ICML*.
- **Optuna Documentation**: Tree-structured Parzen Estimator (TPE) sampler.
---
*Generated by Atomizer Framework. Study cross-links V10 FEA data for surrogate training.*
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