Atomizer/studies/m1_mirror_adaptive_V11/README.md

# 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.*