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feat: Add M1 mirror Zernike optimization with correct RMS calculation

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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>
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2025-11-28 16:30:15 -05:00
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{
"$schema": "Atomizer M1 Mirror Zernike Optimization",
"study_name": "m1_mirror_zernike_optimization",
"description": "Telescope primary mirror support structure optimization using Zernike wavefront error metrics with neural acceleration",
"design_variables": [
{
"name": "lateral_inner_angle",
"expression_name": "lateral_inner_angle",
"min": 25.0,
"max": 28.5,
"baseline": 26.79,
"units": "degrees",
"description": "Lateral support inner angle",
"enabled": false
},
{
"name": "lateral_outer_angle",
"expression_name": "lateral_outer_angle",
"min": 13.0,
"max": 17.0,
"baseline": 14.64,
"units": "degrees",
"description": "Lateral support outer angle",
"enabled": false
},
{
"name": "lateral_outer_pivot",
"expression_name": "lateral_outer_pivot",
"min": 9.0,
"max": 12.0,
"baseline": 10.40,
"units": "mm",
"description": "Lateral outer pivot position",
"enabled": false
},
{
"name": "lateral_inner_pivot",
"expression_name": "lateral_inner_pivot",
"min": 9.0,
"max": 12.0,
"baseline": 10.07,
"units": "mm",
"description": "Lateral inner pivot position",
"enabled": false
},
{
"name": "lateral_middle_pivot",
"expression_name": "lateral_middle_pivot",
"min": 18.0,
"max": 23.0,
"baseline": 20.73,
"units": "mm",
"description": "Lateral middle pivot position",
"enabled": false
},
{
"name": "lateral_closeness",
"expression_name": "lateral_closeness",
"min": 9.5,
"max": 12.5,
"baseline": 11.02,
"units": "mm",
"description": "Lateral support closeness parameter",
"enabled": false
},
{
"name": "whiffle_min",
"expression_name": "whiffle_min",
"min": 35.0,
"max": 55.0,
"baseline": 40.55,
"units": "mm",
"description": "Whiffle tree minimum parameter",
"enabled": true
},
{
"name": "whiffle_outer_to_vertical",
"expression_name": "whiffle_outer_to_vertical",
"min": 68.0,
"max": 80.0,
"baseline": 75.67,
"units": "degrees",
"description": "Whiffle tree outer to vertical angle",
"enabled": true
},
{
"name": "whiffle_triangle_closeness",
"expression_name": "whiffle_triangle_closeness",
"min": 50.0,
"max": 65.0,
"baseline": 60.00,
"units": "mm",
"description": "Whiffle tree triangle closeness",
"enabled": false
},
{
"name": "blank_backface_angle",
"expression_name": "blank_backface_angle",
"min": 3.5,
"max": 5.0,
"baseline": 4.23,
"units": "degrees",
"description": "Mirror blank backface angle",
"enabled": false
},
{
"name": "inner_circular_rib_dia",
"expression_name": "inner_circular_rib_dia",
"min": 480.0,
"max": 620.0,
"baseline": 534.00,
"units": "mm",
"description": "Inner circular rib diameter",
"enabled": true
}
],
"objectives": [
{
"name": "rel_filtered_rms_40_vs_20",
"description": "Filtered RMS WFE at 40 deg relative to 20 deg reference",
"direction": "minimize",
"weight": 5.0,
"target": 4.0,
"units": "nm",
"extractor": "zernike_relative",
"extractor_config": {
"target_subcase": "40",
"reference_subcase": "20",
"metric": "relative_filtered_rms_nm"
}
},
{
"name": "rel_filtered_rms_60_vs_20",
"description": "Filtered RMS WFE at 60 deg relative to 20 deg reference",
"direction": "minimize",
"weight": 5.0,
"target": 10.0,
"units": "nm",
"extractor": "zernike_relative",
"extractor_config": {
"target_subcase": "60",
"reference_subcase": "20",
"metric": "relative_filtered_rms_nm"
}
},
{
"name": "mfg_90_optician_workload",
"description": "Optician workload at 90 deg polishing orientation (filtered RMS with J1-J3)",
"direction": "minimize",
"weight": 1.0,
"target": 20.0,
"units": "nm",
"extractor": "zernike",
"extractor_config": {
"subcase": "90",
"metric": "rms_filter_j1to3",
"reference_subcase": "20"
}
}
],
"constraints": [
{
"name": "max_stress",
"description": "Maximum von Mises stress in mirror assembly",
"type": "upper_bound",
"threshold": 10.0,
"units": "MPa",
"enabled": false
}
],
"zernike_settings": {
"n_modes": 50,
"filter_low_orders": 4,
"displacement_unit": "mm",
"subcases": ["1", "2", "3", "4"],
"subcase_labels": {"1": "90deg", "2": "20deg", "3": "40deg", "4": "60deg"},
"reference_subcase": "2",
"polishing_subcase": "1",
"output_full_coefficients": true,
"_note": "Subcase mapping matches NX: 1=90deg, 2=20deg(ref), 3=40deg, 4=60deg"
},
"optimization_settings": {
"n_trials": 100,
"n_fea_trials": 40,
"n_neural_trials": 500,
"sampler": "TPE",
"seed": 42,
"n_startup_trials": 15,
"tpe_n_ei_candidates": 150,
"tpe_multivariate": true,
"objective_strategy": "weighted_sum",
"objective_direction": "minimize"
},
"surrogate_settings": {
"enabled": true,
"model_type": "ParametricZernikePredictor",
"training_config": {
"hidden_channels": 128,
"num_layers": 4,
"learning_rate": 0.001,
"epochs": 200,
"batch_size": 8,
"train_split": 0.8
},
"outputs": {
"description": "50 Zernike coefficients x 4 subcases = 200 outputs",
"coefficients_per_subcase": 50,
"subcases": ["20", "40", "60", "90"]
}
},
"nx_settings": {
"nx_install_path": "C:\\Program Files\\Siemens\\NX2506",
"model_dir": "C:\\Users\\Antoine\\CADTOMASTE\\Atomizer\\M1-Gigabit\\Latest",
"sim_file": "ASSY_M1_assyfem1_sim1.sim",
"solution_name": "Solution 1",
"solve_all_subcases": true,
"op2_pattern": "*-solution_1.op2",
"op2_timeout_s": 1800,
"op2_stable_s": 5,
"post_solve_delay_s": 5
},
"output_settings": {
"save_zernike_coefficients": true,
"save_field_data": true,
"generate_reports": true,
"archive_results": true
}
}

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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

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# M1 Mirror Zernike Optimization
Multi-objective telescope primary mirror support structure optimization using Zernike wavefront error decomposition with neural network acceleration.
**Created**: 2025-11-28
**Protocol**: Protocol 12 (Hybrid FEA/Neural with Zernike)
**Status**: Setup Complete - Requires Expression Path Fix
---
## 1. Engineering Problem
### 1.1 Objective
Optimize the telescope primary mirror (M1) support structure to minimize wavefront error (WFE) across different gravity orientations (zenith angles), ensuring consistent optical performance from 20° to 90° elevation.
### 1.2 Physical System
- **Component**: M1 primary mirror assembly with whiffle tree support
- **Material**: Borosilicate glass (mirror blank), steel (support structure)
- **Loading**: Gravity at multiple zenith angles (20°, 40°, 60°, 90°)
- **Boundary Conditions**: Whiffle tree kinematic mount
- **Analysis Type**: Linear static multi-subcase (Nastran SOL 101)
- **Output**: Surface deformation → Zernike polynomial decomposition
---
## 2. Mathematical Formulation
### 2.1 Objectives
| Objective | Goal | Weight | Formula | Units | Target |
|-----------|------|--------|---------|-------|--------|
| rel_filtered_rms_40_vs_20 | minimize | 5.0 | $\sigma_{40/20} = \sqrt{\sum_{j=5}^{50} (Z_j^{rel})^2}$ | nm | 4 nm |
| rel_filtered_rms_60_vs_20 | minimize | 5.0 | $\sigma_{60/20} = \sqrt{\sum_{j=5}^{50} (Z_j^{rel})^2}$ | nm | 10 nm |
| mfg_90_optician_workload | minimize | 1.0 | $\sigma_{90}^{J4+} = \sqrt{\sum_{j=4}^{50} (Z_j^{rel})^2}$ | nm | 20 nm |
Where:
- $Z_j^{rel}$ = Relative Zernike coefficient (target subcase minus reference)
- Filtered RMS excludes J1-J4 (piston, tip, tilt, defocus) - correctable by alignment
- Manufacturing workload keeps J4 (defocus) since it represents optician correction effort
### 2.2 Zernike Decomposition
The wavefront error $W(r,\theta)$ is decomposed into Zernike polynomials:
$$W(r,\theta) = \sum_{j=1}^{50} Z_j \cdot P_j(r,\theta)$$
Where $P_j$ are Noll-indexed Zernike polynomials on the unit disk.
**WFE from Displacement**:
$$W_{nm} = 2 \cdot \delta_z \cdot 10^6$$
Where $\delta_z$ is the Z-displacement in mm (factor of 2 for reflection).
### 2.3 Design Variables
| Parameter | Symbol | Bounds | Baseline | Units | Description |
|-----------|--------|--------|----------|-------|-------------|
| whiffle_min | $w_{min}$ | [35, 55] | 40.55 | mm | Whiffle tree minimum parameter |
| whiffle_outer_to_vertical | $\alpha$ | [68, 80] | 75.67 | deg | Outer support angle to vertical |
| inner_circular_rib_dia | $D_{rib}$ | [480, 620] | 534.00 | mm | Inner circular rib diameter |
**Design Space**:
$$\mathbf{x} = [w_{min}, \alpha, D_{rib}]^T \in \mathbb{R}^3$$
**Additional Variables (Disabled)**:
- lateral_inner_angle, lateral_outer_angle (lateral support angles)
- lateral_outer_pivot, lateral_inner_pivot, lateral_middle_pivot (pivot positions)
- lateral_closeness (lateral support spacing)
- whiffle_triangle_closeness (whiffle tree geometry)
- blank_backface_angle (mirror blank geometry)
### 2.4 Objective Strategy
**Weighted Sum Minimization**:
$$J(\mathbf{x}) = \sum_{i=1}^{3} w_i \cdot \frac{f_i(\mathbf{x})}{t_i}$$
Where:
- $w_i$ = weight for objective $i$
- $f_i(\mathbf{x})$ = objective value
- $t_i$ = target value (normalization)
---
## 3. Optimization Algorithm
### 3.1 TPE Configuration
| Parameter | Value | Description |
|-----------|-------|-------------|
| Algorithm | TPE | Tree-structured Parzen Estimator |
| Sampler | `TPESampler` | Bayesian optimization |
| n_startup_trials | 15 | Random exploration before modeling |
| n_ei_candidates | 150 | Expected improvement candidates |
| multivariate | true | Model parameter correlations |
| Trials | 100 | 40 FEA + neural acceleration |
| Seed | 42 | Reproducibility |
**TPE Properties**:
- Models $p(x|y<y^*)$ and $p(x|y \geq y^*)$ separately
- Expected Improvement: $EI(x) = \int_{-\infty}^{y^*} (y^* - y) p(y|x) dy$
- Handles high-dimensional continuous spaces efficiently
### 3.2 Return Format
```python
def fea_objective(trial) -> Tuple[float, dict]:
# ... simulation and Zernike extraction ...
weighted_obj = compute_weighted_objective(objectives, config)
return weighted_obj, trial_data
```
---
## 4. Simulation Pipeline
### 4.1 Trial Execution Flow
```
┌─────────────────────────────────────────────────────────────────────┐
│ TRIAL n EXECUTION │
├─────────────────────────────────────────────────────────────────────┤
│ │
│ 1. OPTUNA SAMPLES (TPE) │
│ whiffle_min = trial.suggest_float("whiffle_min", 35, 55) │
│ whiffle_outer_to_vertical = trial.suggest_float(..., 68, 80) │
│ inner_circular_rib_dia = trial.suggest_float(..., 480, 620) │
│ │
│ 2. NX PARAMETER UPDATE │
│ Module: optimization_engine/solve_simulation.py │
│ Target Part: M1_Blank.prt │
│ Action: Update expressions with new design values │
│ │
│ 3. NX SIMULATION (Nastran SOL 101 - 4 Subcases) │
│ Module: optimization_engine/solve_simulation.py │
│ Input: ASSY_M1_assyfem1_sim1.sim │
│ Subcases: 1=20°, 2=40°, 3=60°, 4=90° zenith │
│ Output: .dat, .op2, .f06 │
│ │
│ 4. ZERNIKE EXTRACTION (Displacement-Based) │
│ a. Read node coordinates from BDF/DAT │
│ b. Read Z-displacements from OP2 for each subcase │
│ c. Compute RELATIVE displacement (subcase - reference) │
│ d. Convert to WFE: W = 2 * Δδz * 10^6 nm │
│ e. Fit 50 Zernike coefficients via least-squares │
│ f. Compute filtered RMS (exclude J1-J4) │
│ │
│ 5. OBJECTIVE COMPUTATION │
│ rel_filtered_rms_40_vs_20 ← Zernike RMS (subcase 2 - 1) │
│ rel_filtered_rms_60_vs_20 ← Zernike RMS (subcase 3 - 1) │
│ mfg_90_optician_workload ← Zernike RMS J4+ (subcase 4 - 1) │
│ │
│ 6. WEIGHTED SUM │
│ J = Σ (weight × objective / target) │
│ │
│ 7. RETURN TO OPTUNA │
│ return weighted_objective │
│ │
└─────────────────────────────────────────────────────────────────────┘
```
### 4.2 Multi-Subcase Structure
| Subcase | Zenith Angle | Role | Description |
|---------|--------------|------|-------------|
| 1 | 20° | Reference | Near-zenith baseline orientation |
| 2 | 40° | Target | Mid-elevation performance |
| 3 | 60° | Target | Low-elevation performance |
| 4 | 90° | Polishing | Horizontal (manufacturing reference) |
---
## 5. Result Extraction Methods
### 5.1 Zernike Extraction (Displacement-Based Subtraction)
| Attribute | Value |
|-----------|-------|
| **Method** | `extract_zernike_with_relative()` |
| **Location** | `run_optimization.py` (inline) |
| **Geometry Source** | `.dat` (BDF format) |
| **Displacement Source** | `.op2` (OP2 binary) |
| **Output** | 50 Zernike coefficients per subcase |
**Algorithm (Correct Approach - Matches Original Script)**:
1. **Load Geometry**: Read node coordinates $(X_i, Y_i)$ from BDF
2. **Load Displacements**: Read $\delta_{z,i}$ from OP2 for each subcase
3. **Compute Relative Displacement** (node-by-node):
$$\Delta\delta_{z,i} = \delta_{z,i}^{target} - \delta_{z,i}^{reference}$$
4. **Convert to WFE**:
$$W_i = 2 \cdot \Delta\delta_{z,i} \cdot 10^6 \text{ nm}$$
5. **Fit Zernike** (least-squares on unit disk):
$$\min_{\mathbf{Z}} \| \mathbf{W} - \mathbf{P} \mathbf{Z} \|^2$$
6. **Compute RMS**:
$$\sigma_{filtered} = \sqrt{\sum_{j=5}^{50} Z_j^2}$$
**Critical Implementation Note**:
The relative calculation MUST subtract displacements first, then fit Zernike - NOT subtract Zernike coefficients directly. This matches the original `zernike_Post_Script_NX.py` implementation.
### 5.2 Code Pattern
```python
from pyNastran.op2.op2 import OP2
from pyNastran.bdf.bdf import BDF
# Read geometry
bdf = BDF()
bdf.read_bdf(str(bdf_path))
node_geo = {nid: node.get_position() for nid, node in bdf.nodes.items()}
# Read displacements
op2 = OP2()
op2.read_op2(str(op2_path))
# Compute relative displacement (node-by-node)
for i, nid in enumerate(node_ids):
rel_dz = disp_z_target[i] - disp_z_reference[nid]
# Convert to WFE and fit Zernike
rel_wfe_nm = 2.0 * rel_disp_z * 1e6
coeffs, R_max = compute_zernike_from_wfe(X, Y, rel_wfe_nm, n_modes=50)
```
---
## 6. Neural Acceleration (AtomizerField)
### 6.1 Configuration
| Setting | Value | Description |
|---------|-------|-------------|
| `enabled` | `true` | Neural surrogate active |
| `model_type` | `ParametricZernikePredictor` | Predicts Zernike coefficients |
| `hidden_channels` | 128 | MLP width |
| `num_layers` | 4 | MLP depth |
| `learning_rate` | 0.001 | Adam optimizer |
| `epochs` | 200 | Training iterations |
| `batch_size` | 8 | Mini-batch size |
| `train_split` | 0.8 | Training fraction |
### 6.2 Surrogate Model
**Input**: $\mathbf{x} = [w_{min}, \alpha, D_{rib}]^T \in \mathbb{R}^3$
**Output**: $\hat{\mathbf{Z}} \in \mathbb{R}^{200}$ (50 coefficients × 4 subcases)
**Architecture**: Multi-Layer Perceptron
```
Input(3) → Linear(128) → ReLU → Linear(128) → ReLU →
Linear(128) → ReLU → Linear(128) → ReLU → Linear(200)
```
**Training Objective**:
$$\mathcal{L} = \frac{1}{N} \sum_{i=1}^{N} \| \mathbf{Z}_i - \hat{\mathbf{Z}}_i \|^2$$
### 6.3 Training Data Location
```
studies/m1_mirror_zernike_optimization/2_results/zernike_surrogate/
├── checkpoint_best.pt # Best model weights
├── training_history.json # Loss curves
└── validation_metrics.json # R², MAE per coefficient
```
### 6.4 Expected Performance
| Metric | Value |
|--------|-------|
| FEA time per trial | 10-15 min |
| Neural time per trial | ~10 ms |
| Speedup | ~60,000x |
| Expected R² | > 0.95 (after 40 samples) |
---
## 7. Study File Structure
```
m1_mirror_zernike_optimization/
│
├── 1_setup/ # INPUT CONFIGURATION
│ ├── model/ # NX Model Files (symlinked/referenced)
│ │ └── → C:\Users\Antoine\CADTOMASTE\Atomizer\M1-Gigabit\Latest\
│ │ ├── ASSY_M1.prt # Top-level assembly
│ │ ├── M1_Blank.prt # Mirror blank (EXPRESSIONS HERE)
│ │ ├── ASSY_M1_assyfem1.afm # Assembly FEM
│ │ ├── ASSY_M1_assyfem1_sim1.sim # Simulation file
│ │ └── assy_m1_assyfem1_sim1-solution_1.op2 # Results
│ │
│ └── optimization_config.json # Study configuration
│
├── 2_results/ # OUTPUT (auto-generated)
│ ├── study.db # Optuna SQLite database
│ ├── zernike_surrogate/ # Neural model checkpoints
│ └── reports/ # Generated reports
│
├── run_optimization.py # Main entry point
├── DASHBOARD.md # Quick reference
└── README.md # This blueprint
```
---
## 8. Results Location
After optimization completes, results are stored in `2_results/`:
| File | Description | Format |
|------|-------------|--------|
| `study.db` | Optuna database with all trials | SQLite |
| `zernike_surrogate/checkpoint_best.pt` | Trained neural model | PyTorch |
| `reports/optimization_report.md` | Full results report | Markdown |
### 8.1 Results Report Contents
The generated report will contain:
1. **Optimization Summary** - Best WFE configurations found
2. **Zernike Analysis** - Coefficient distributions per subcase
3. **Parameter Sensitivity** - Design variable vs WFE relationships
4. **Convergence History** - Weighted objective over trials
5. **Neural Surrogate Performance** - R² per Zernike mode
6. **Recommended Configurations** - Top designs for production
### 8.2 Zernike-Specific Analysis
| Mode | Name | Physical Meaning |
|------|------|------------------|
| J1 | Piston | Constant offset (ignored) |
| J2, J3 | Tip/Tilt | Angular misalignment (correctable) |
| J4 | Defocus | Power error (correctable) |
| J5, J6 | Astigmatism | Cylindrical error |
| J7, J8 | Coma | Off-axis aberration |
| J9-J11 | Trefoil, Spherical | Higher-order terms |
---
## 9. Quick Start
### Staged Workflow (Recommended)
```bash
cd studies/m1_mirror_zernike_optimization
# Check current status
python run_optimization.py --status
# Run FEA trials (builds training data)
python run_optimization.py --run --trials 40
# Train neural surrogate
python run_optimization.py --train-surrogate
# Run neural-accelerated optimization
python run_optimization.py --run --trials 500 --enable-nn
```
### Stage Descriptions
| Stage | Command | Purpose | When to Use |
|-------|---------|---------|-------------|
| **STATUS** | `--status` | Check database, trial count | Anytime |
| **RUN** | `--run --trials N` | Run FEA optimization | Initial exploration |
| **TRAIN** | `--train-surrogate` | Train neural model | After ~40 FEA trials |
| **NEURAL** | `--run --enable-nn` | Fast neural trials | After training |
### Dashboard Access
| Dashboard | URL | Purpose |
|-----------|-----|---------|
| **Optuna Dashboard** | `optuna-dashboard sqlite:///2_results/study.db` | Trial history |
```bash
# Launch Optuna dashboard
cd studies/m1_mirror_zernike_optimization
optuna-dashboard sqlite:///2_results/study.db --port 8081
# Open http://localhost:8081
```
---
## 10. Configuration Reference
**File**: `1_setup/optimization_config.json`
| Section | Key | Description |
|---------|-----|-------------|
| `design_variables[]` | 11 parameters | 3 enabled, 8 disabled |
| `objectives[]` | 3 WFE metrics | Relative filtered RMS |
| `zernike_settings.n_modes` | 50 | Zernike polynomial count |
| `zernike_settings.filter_low_orders` | 4 | Exclude J1-J4 |
| `zernike_settings.subcases` | ["1","2","3","4"] | OP2 subcase IDs |
| `zernike_settings.reference_subcase` | "1" | 20° baseline |
| `optimization_settings.n_trials` | 100 | Total FEA trials |
| `surrogate_settings.model_type` | ParametricZernikePredictor | Neural architecture |
| `nx_settings.model_dir` | M1-Gigabit/Latest | NX model location |
| `nx_settings.sim_file` | ASSY_M1_assyfem1_sim1.sim | Simulation file |
---
## 11. Known Issues & Solutions
### 11.1 Expression Update Failure
**Issue**: NX journal cannot find expressions in assembly FEM.
**Cause**: Expressions are in component part `M1_Blank.prt`, not in `ASSY_M1_assyfem1`.
**Solution**: The `solve_simulation.py` journal now searches for `M1_Blank` part to update expressions. If still failing, verify:
1. `M1_Blank.prt` is loaded in the assembly
2. Expression names match exactly (case-sensitive)
3. Part is not read-only
### 11.2 Subcase Numbering
**Issue**: OP2 file uses numeric subcases (1,2,3,4) not angle labels (20,40,60,90).
**Solution**: Config uses `subcases: ["1","2","3","4"]` with `subcase_labels` mapping.
---
## 12. References
- **Noll, R.J.** (1976). Zernike polynomials and atmospheric turbulence. *JOSA*.
- **Wilson, R.N.** (2004). *Reflecting Telescope Optics I*. Springer.
- **pyNastran Documentation**: BDF/OP2 parsing for FEA post-processing
- **Optuna Documentation**: TPE sampler for black-box optimization

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