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>
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# Protocol 13: Adaptive Multi-Objective Optimization
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## Overview
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Protocol 13 implements an adaptive multi-objective optimization strategy that combines:
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- **FEA (Finite Element Analysis)** for ground truth simulations
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- **Neural Network Surrogates** for rapid exploration
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- **Iterative refinement** with periodic retraining
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This protocol is ideal for expensive simulations where each FEA run takes significant time (minutes to hours), but you need to explore a large design space efficiently.
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## When to Use Protocol 13
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| Scenario | Recommended |
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|----------|-------------|
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| FEA takes > 5 minutes per run | Yes |
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| Need to explore > 100 designs | Yes |
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| Multi-objective optimization (2-4 objectives) | Yes |
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| Single objective, fast FEA (< 1 min) | No, use Protocol 10/11 |
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| Highly nonlinear response surfaces | Yes, with more FEA samples |
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## Architecture
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```
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┌─────────────────────────────────────────────────────────────────┐
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│ Adaptive Optimization Loop │
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├─────────────────────────────────────────────────────────────────┤
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│ │
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│ Iteration 1: │
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│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
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│ │ Initial FEA │ -> │ Train NN │ -> │ NN Search │ │
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│ │ (50-100) │ │ Surrogate │ │ (1000 trials)│ │
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│ └──────────────┘ └──────────────┘ └──────────────┘ │
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│ │ │
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│ v │
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│ Iteration 2+: │
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│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
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│ │ Validate Top │ -> │ Retrain NN │ -> │ NN Search │ │
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│ │ NN with FEA │ │ with new data│ │ (1000 trials)│ │
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│ └──────────────┘ └──────────────┘ └──────────────┘ │
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│ │
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└─────────────────────────────────────────────────────────────────┘
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```
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## Configuration
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### optimization_config.json
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```json
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{
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"study_name": "my_adaptive_study",
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"protocol": 13,
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"adaptive_settings": {
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"enabled": true,
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"initial_fea_trials": 50,
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"nn_trials_per_iteration": 1000,
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"fea_validation_per_iteration": 5,
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"max_iterations": 10,
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"convergence_threshold": 0.01,
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"retrain_epochs": 100
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},
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"objectives": [
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{
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"name": "thermal_40_vs_20",
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"direction": "minimize",
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"weight": 1.0
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},
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{
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"name": "thermal_60_vs_20",
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"direction": "minimize",
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"weight": 0.5
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},
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{
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"name": "manufacturability",
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"direction": "minimize",
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"weight": 0.3
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}
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],
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"design_variables": [
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{
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"name": "rib_thickness",
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"expression_name": "rib_thickness",
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"min": 5.0,
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"max": 15.0,
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"baseline": 10.0
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}
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],
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"surrogate_settings": {
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"enabled": true,
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"model_type": "neural_network",
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"hidden_layers": [128, 64, 32],
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"learning_rate": 0.001,
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"batch_size": 32
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}
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}
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```
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### Key Parameters
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| Parameter | Description | Recommended |
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|-----------|-------------|-------------|
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| `initial_fea_trials` | FEA runs before first NN training | 50-100 |
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| `nn_trials_per_iteration` | NN-predicted trials per iteration | 500-2000 |
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| `fea_validation_per_iteration` | Top NN trials validated with FEA | 3-10 |
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| `max_iterations` | Maximum adaptive iterations | 5-20 |
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| `convergence_threshold` | Stop if improvement < threshold | 0.01 (1%) |
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## Workflow
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### Phase 1: Initial FEA Sampling
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```python
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# Generates space-filling Latin Hypercube samples
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# Runs FEA on each sample
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# Stores results in Optuna database with source='FEA'
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```
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### Phase 2: Neural Network Training
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```python
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# Extracts all FEA trials from database
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# Normalizes inputs (design variables) to [0, 1]
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# Trains multi-output neural network
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# Validates on held-out set (20%)
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```
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### Phase 3: NN-Accelerated Search
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```python
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# Uses trained NN as objective function
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# Runs NSGA-II with 1000+ trials (fast, ~ms per trial)
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# Identifies Pareto-optimal candidates
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# Stores predictions with source='NN'
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```
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### Phase 4: FEA Validation
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```python
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# Selects top N NN predictions
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# Runs actual FEA on these candidates
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# Updates database with ground truth
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# Checks for improvement
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```
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### Phase 5: Iteration
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```python
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# If improved: retrain NN with new FEA data
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# If converged: stop and report best
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# Otherwise: continue to next iteration
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```
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## Output Files
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```
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studies/my_study/
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├── 3_results/
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│ ├── study.db # Optuna database (all trials)
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│ ├── adaptive_state.json # Current iteration state
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│ ├── surrogate_model.pt # Trained neural network
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│ ├── training_history.json # NN training metrics
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│ └── STUDY_REPORT.md # Generated summary report
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```
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### adaptive_state.json
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```json
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{
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"iteration": 3,
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"total_fea_count": 103,
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"total_nn_count": 3000,
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"best_weighted": 1.456,
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"best_params": {
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"rib_thickness": 8.5,
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"...": "..."
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},
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"history": [
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{"iteration": 1, "fea_count": 50, "nn_count": 1000, "improved": true},
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{"iteration": 2, "fea_count": 55, "nn_count": 2000, "improved": true},
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{"iteration": 3, "fea_count": 103, "nn_count": 3000, "improved": false}
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]
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}
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```
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## Dashboard Integration
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Protocol 13 studies display in the Atomizer dashboard with:
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- **FEA vs NN Differentiation**: Blue circles for FEA, orange crosses for NN
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- **Pareto Front Highlighting**: Green markers for Pareto-optimal solutions
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- **Convergence Plot**: Shows optimization progress with best-so-far line
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- **Parallel Coordinates**: Filter and explore the design space
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- **Parameter Importance**: Correlation-based sensitivity analysis
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## Best Practices
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### 1. Initial Sampling Strategy
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Use Latin Hypercube Sampling (LHS) for initial FEA trials to ensure good coverage:
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```python
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sampler = optuna.samplers.LatinHypercubeSampler(seed=42)
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```
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### 2. Neural Network Architecture
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For most problems, start with:
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- 2-3 hidden layers
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- 64-128 neurons per layer
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- ReLU activation
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- Adam optimizer with lr=0.001
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### 3. Validation Strategy
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Always validate top NN predictions with FEA before trusting them:
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- NN predictions can be wrong in unexplored regions
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- FEA validation provides ground truth
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- More FEA = more accurate NN (trade-off with time)
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### 4. Convergence Criteria
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Stop when:
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- No improvement for 2-3 consecutive iterations
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- Reached FEA budget limit
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- Objective improvement < 1% threshold
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## Example: M1 Mirror Optimization
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```bash
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# Start adaptive optimization
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cd studies/m1_mirror_adaptive_V11
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python run_optimization.py --start
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# Monitor progress
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python run_optimization.py --status
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# Generate report
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python generate_report.py
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```
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Results after 3 iterations:
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- 103 FEA trials
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- 3000 NN trials
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- Best thermal 40°C vs 20°C: 5.99 nm RMS
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- Best thermal 60°C vs 20°C: 14.02 nm RMS
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## Troubleshooting
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### NN Predictions Don't Match FEA
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- Increase initial FEA samples
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- Add more hidden layers
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- Check for outliers in training data
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- Ensure proper normalization
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### Optimization Not Converging
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- Increase NN trials per iteration
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- Check objective function implementation
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- Verify design variable bounds
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- Consider adding constraints
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### Memory Issues
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- Reduce `nn_trials_per_iteration`
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- Use batch processing for large datasets
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- Clear trial cache periodically
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## Related Documentation
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- [Protocol 11: Multi-Objective NSGA-II](./PROTOCOL_11_MULTI_OBJECTIVE.md)
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- [Protocol 12: Hybrid FEA/NN](./PROTOCOL_12_HYBRID.md)
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- [Neural Surrogate Training](../07_DEVELOPMENT/NEURAL_SURROGATE.md)
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- [Zernike Extractor](./ZERNIKE_EXTRACTOR.md)
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