# 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