feat: Add MLP surrogate with Turbo Mode for 100x faster optimization
Neural Acceleration (MLP Surrogate): - Add run_nn_optimization.py with hybrid FEA/NN workflow - MLP architecture: 4-layer (64->128->128->64) with BatchNorm/Dropout - Three workflow modes: - --all: Sequential export->train->optimize->validate - --hybrid-loop: Iterative Train->NN->Validate->Retrain cycle - --turbo: Aggressive single-best validation (RECOMMENDED) - Turbo mode: 5000 NN trials + 50 FEA validations in ~12 minutes - Separate nn_study.db to avoid overloading dashboard Performance Results (bracket_pareto_3obj study): - NN prediction errors: mass 1-5%, stress 1-4%, stiffness 5-15% - Found minimum mass designs at boundary (angle~30deg, thick~30mm) - 100x speedup vs pure FEA exploration Protocol Operating System: - Add .claude/skills/ with Bootstrap, Cheatsheet, Context Loader - Add docs/protocols/ with operations (OP_01-06) and system (SYS_10-14) - Update SYS_14_NEURAL_ACCELERATION.md with MLP Turbo Mode docs NX Automation: - Add optimization_engine/hooks/ for NX CAD/CAE automation - Add study_wizard.py for guided study creation - Fix FEM mesh update: load idealized part before UpdateFemodel() New Study: - bracket_pareto_3obj: 3-objective Pareto (mass, stress, stiffness) - 167 FEA trials + 5000 NN trials completed - Demonstrates full hybrid workflow 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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Antoine
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.claude/skills/01_CHEATSHEET.md
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# Atomizer Quick Reference Cheatsheet
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**Version**: 1.0
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**Purpose**: Rapid lookup for common operations. "I want X → Use Y"
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---
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## Task → Protocol Quick Lookup
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| I want to... | Use Protocol | Key Command/Action |
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|--------------|--------------|-------------------|
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| Create a new optimization study | OP_01 | Generate `optimization_config.json` + `run_optimization.py` |
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| Run an optimization | OP_02 | `conda activate atomizer && python run_optimization.py` |
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| Check optimization progress | OP_03 | Query `study.db` or check dashboard at `localhost:3000` |
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| See best results | OP_04 | `optuna-dashboard sqlite:///study.db` or dashboard |
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| Export neural training data | OP_05 | `python run_optimization.py --export-training` |
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| Fix an error | OP_06 | Read error log → follow diagnostic tree |
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| Add custom physics extractor | EXT_01 | Create in `optimization_engine/extractors/` |
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| Add lifecycle hook | EXT_02 | Create in `optimization_engine/plugins/` |
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---
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## Extractor Quick Reference
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| Physics | Extractor | Function Call |
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|---------|-----------|---------------|
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| Max displacement | E1 | `extract_displacement(op2_file, subcase=1)` |
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| Natural frequency | E2 | `extract_frequency(op2_file, subcase=1, mode_number=1)` |
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| Von Mises stress | E3 | `extract_solid_stress(op2_file, subcase=1, element_type='cquad4')` |
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| BDF mass | E4 | `extract_mass_from_bdf(bdf_file)` |
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| CAD expression mass | E5 | `extract_mass_from_expression(prt_file, expression_name='p173')` |
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| Field data | E6 | `FieldDataExtractor(field_file, result_column, aggregation)` |
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| Stiffness (k=F/δ) | E7 | `StiffnessCalculator(...)` |
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| Zernike WFE | E8 | `extract_zernike_from_op2(op2_file, bdf_file, subcase)` |
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| Zernike relative | E9 | `extract_zernike_relative_rms(op2_file, bdf_file, target, ref)` |
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| Zernike builder | E10 | `ZernikeObjectiveBuilder(op2_finder)` |
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| Part mass + material | E11 | `extract_part_mass_material(prt_file)` → mass, volume, material |
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**Full details**: See `SYS_12_EXTRACTOR_LIBRARY.md` or `modules/extractors-catalog.md`
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---
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## Protocol Selection Guide
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### Single Objective Optimization
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```
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Question: Do you have ONE goal to minimize/maximize?
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├─ Yes, simple problem (smooth, <10 params)
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│ └─► Protocol 10 + CMA-ES or GP-BO sampler
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│
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├─ Yes, complex problem (noisy, many params)
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│ └─► Protocol 10 + TPE sampler
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│
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└─ Not sure about problem characteristics?
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└─► Protocol 10 with adaptive characterization (default)
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```
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### Multi-Objective Optimization
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```
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Question: Do you have 2-3 competing goals?
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├─ Yes (e.g., minimize mass AND minimize stress)
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│ └─► Protocol 11 + NSGA-II sampler
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│
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└─ Pareto front needed?
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└─► Protocol 11 (returns best_trials, not best_trial)
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```
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### Neural Network Acceleration
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```
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Question: Do you need >50 trials OR surrogate model?
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├─ Yes
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│ └─► Protocol 14 (configure surrogate_settings in config)
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│
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└─ Training data export needed?
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└─► OP_05_EXPORT_TRAINING_DATA.md
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```
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---
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## Configuration Quick Reference
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### optimization_config.json Structure
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```json
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{
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"study_name": "my_study",
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"design_variables": [
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{"name": "thickness", "min": 1.0, "max": 10.0, "unit": "mm"}
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],
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"objectives": [
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{"name": "mass", "goal": "minimize", "unit": "kg"}
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],
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"constraints": [
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{"name": "max_stress", "type": "<=", "threshold": 250, "unit": "MPa"}
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],
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"optimization_settings": {
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"protocol": "protocol_10_single_objective",
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"sampler": "TPESampler",
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"n_trials": 50
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},
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"simulation": {
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"model_file": "model.prt",
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"sim_file": "model.sim",
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"solver": "nastran"
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}
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}
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```
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### Sampler Quick Selection
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| Sampler | Use When | Protocol |
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|---------|----------|----------|
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| `TPESampler` | Default, robust to noise | P10 |
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| `CMAESSampler` | Smooth, unimodal problems | P10 |
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| `GPSampler` | Expensive FEA, few trials | P10 |
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| `NSGAIISampler` | Multi-objective (2-3 goals) | P11 |
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| `RandomSampler` | Characterization phase only | P10 |
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---
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## Study File Structure
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```
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studies/{study_name}/
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├── 1_setup/
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│ ├── model/ # NX files (.prt, .sim, .fem)
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│ └── optimization_config.json
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├── 2_results/
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│ ├── study.db # Optuna SQLite database
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│ ├── optimizer_state.json # Real-time state (P13)
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│ └── trial_logs/
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├── README.md # MANDATORY: Engineering blueprint
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├── STUDY_REPORT.md # MANDATORY: Results tracking
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└── run_optimization.py # Entrypoint script
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```
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---
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## Common Commands
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```bash
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# Activate environment (ALWAYS FIRST)
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conda activate atomizer
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# Run optimization
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python run_optimization.py
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# Run with specific trial count
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python run_optimization.py --n-trials 100
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# Resume interrupted optimization
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python run_optimization.py --resume
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# Export training data for neural network
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python run_optimization.py --export-training
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# View results in Optuna dashboard
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optuna-dashboard sqlite:///2_results/study.db
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# Check study status
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python -c "import optuna; s=optuna.load_study('my_study', 'sqlite:///2_results/study.db'); print(f'Trials: {len(s.trials)}')"
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```
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---
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## Error Quick Fixes
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| Error | Likely Cause | Quick Fix |
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|-------|--------------|-----------|
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| "No module named optuna" | Wrong environment | `conda activate atomizer` |
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| "NX session timeout" | Model too complex | Increase `timeout` in config |
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| "OP2 file not found" | Solve failed | Check NX log for errors |
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| "No feasible solutions" | Constraints too tight | Relax constraint thresholds |
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| "NSGA-II requires >1 objective" | Wrong protocol | Use P10 for single-objective |
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| "Expression not found" | Wrong parameter name | Verify expression names in NX |
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| **All trials identical results** | **Missing `*_i.prt`** | **Copy idealized part to study folder!** |
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**Full troubleshooting**: See `OP_06_TROUBLESHOOT.md`
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---
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## CRITICAL: NX FEM Mesh Update
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**If all optimization trials produce identical results, the mesh is NOT updating!**
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### Required Files for Mesh Updates
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```
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studies/{study}/1_setup/model/
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├── Model.prt # Geometry
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├── Model_fem1_i.prt # Idealized part ← MUST EXIST!
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├── Model_fem1.fem # FEM
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└── Model_sim1.sim # Simulation
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```
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### Why It Matters
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The `*_i.prt` (idealized part) MUST be:
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1. **Present** in the study folder
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2. **Loaded** before `UpdateFemodel()` (already implemented in `solve_simulation.py`)
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Without it, `UpdateFemodel()` runs but the mesh doesn't change!
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---
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## Privilege Levels
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| Level | Can Create Studies | Can Add Extractors | Can Add Protocols |
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|-------|-------------------|-------------------|------------------|
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| user | ✓ | ✗ | ✗ |
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| power_user | ✓ | ✓ | ✗ |
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| admin | ✓ | ✓ | ✓ |
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---
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## Dashboard URLs
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| Service | URL | Purpose |
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|---------|-----|---------|
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| Atomizer Dashboard | `http://localhost:3000` | Real-time optimization monitoring |
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| Optuna Dashboard | `http://localhost:8080` | Trial history, parameter importance |
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| API Backend | `http://localhost:5000` | REST API for dashboard |
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---
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## Protocol Numbers Reference
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| # | Name | Purpose |
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|---|------|---------|
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| 10 | IMSO | Intelligent Multi-Strategy Optimization (adaptive) |
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| 11 | Multi-Objective | NSGA-II for Pareto optimization |
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| 12 | - | (Reserved) |
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| 13 | Dashboard | Real-time tracking and visualization |
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| 14 | Neural | Surrogate model acceleration |
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