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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docs/protocols/operations/OP_02_RUN_OPTIMIZATION.md
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# OP_02: Run Optimization
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<!--
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PROTOCOL: Run Optimization
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LAYER: Operations
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VERSION: 1.0
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STATUS: Active
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LAST_UPDATED: 2025-12-05
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PRIVILEGE: user
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LOAD_WITH: []
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-->
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## Overview
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This protocol covers executing optimization runs, including pre-flight validation, execution modes, monitoring, and handling common issues.
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---
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## When to Use
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| Trigger | Action |
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|---------|--------|
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| "start", "run", "execute" | Follow this protocol |
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| "begin optimization" | Follow this protocol |
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| Study setup complete | Execute this protocol |
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---
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## Quick Reference
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**Start Command**:
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```bash
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conda activate atomizer
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cd studies/{study_name}
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python run_optimization.py
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```
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**Common Options**:
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| Flag | Purpose |
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|------|---------|
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| `--n-trials 100` | Override trial count |
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| `--resume` | Continue interrupted run |
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| `--test` | Run single trial for validation |
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| `--export-training` | Export data for neural training |
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---
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## Pre-Flight Checklist
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Before running, verify:
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- [ ] **Environment**: `conda activate atomizer`
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- [ ] **Config exists**: `1_setup/optimization_config.json`
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- [ ] **Script exists**: `run_optimization.py`
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- [ ] **Model files**: NX files in `1_setup/model/`
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- [ ] **No conflicts**: No other optimization running on same study
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- [ ] **Disk space**: Sufficient for results
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**Quick Validation**:
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```bash
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python run_optimization.py --test
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```
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This runs a single trial to verify setup.
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---
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## Execution Modes
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### 1. Standard Run
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```bash
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python run_optimization.py
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```
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Uses settings from `optimization_config.json`.
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### 2. Override Trials
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```bash
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python run_optimization.py --n-trials 100
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```
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Override trial count from config.
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### 3. Resume Interrupted
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```bash
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python run_optimization.py --resume
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```
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Continues from last completed trial.
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### 4. Neural Acceleration
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```bash
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python run_optimization.py --neural
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```
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Requires trained surrogate model.
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### 5. Export Training Data
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```bash
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python run_optimization.py --export-training
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```
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Saves BDF/OP2 for neural network training.
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---
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## Monitoring Progress
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### Option 1: Console Output
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The script prints progress:
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```
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Trial 15/50 complete. Best: 0.234 kg
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Trial 16/50 complete. Best: 0.234 kg
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```
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### Option 2: Dashboard
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See [SYS_13_DASHBOARD_TRACKING](../system/SYS_13_DASHBOARD_TRACKING.md).
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```bash
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# Start dashboard (separate terminal)
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cd atomizer-dashboard/backend && python -m uvicorn api.main:app --port 8000
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cd atomizer-dashboard/frontend && npm run dev
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# Open browser
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http://localhost:3000
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```
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### Option 3: Query Database
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```bash
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python -c "
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import optuna
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study = optuna.load_study('study_name', 'sqlite:///2_results/study.db')
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print(f'Trials: {len(study.trials)}')
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print(f'Best value: {study.best_value}')
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"
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```
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### Option 4: Optuna Dashboard
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```bash
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optuna-dashboard sqlite:///2_results/study.db
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# Open http://localhost:8080
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```
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---
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## During Execution
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### What Happens Per Trial
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1. **Sample parameters**: Optuna suggests design variable values
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2. **Update model**: NX expressions updated via journal
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3. **Solve**: NX Nastran runs FEA simulation
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4. **Extract results**: Extractors read OP2 file
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5. **Evaluate**: Check constraints, compute objectives
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6. **Record**: Trial stored in Optuna database
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### Normal Output
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```
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[2025-12-05 10:15:30] Trial 1 started
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[2025-12-05 10:17:45] NX solve complete (135.2s)
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[2025-12-05 10:17:46] Extraction complete
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[2025-12-05 10:17:46] Trial 1 complete: mass=0.342 kg, stress=198.5 MPa
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[2025-12-05 10:17:47] Trial 2 started
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...
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```
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### Expected Timing
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| Operation | Typical Time |
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|-----------|--------------|
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| NX solve | 30s - 30min |
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| Extraction | <1s |
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| Per trial total | 1-30 min |
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| 50 trials | 1-24 hours |
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---
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## Handling Issues
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### Trial Failed / Pruned
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```
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[WARNING] Trial 12 pruned: Stress constraint violated (312.5 MPa > 250 MPa)
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```
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**Normal behavior** - optimizer learns from failures.
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### NX Session Timeout
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```
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[ERROR] NX session timeout after 600s
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```
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**Solution**: Increase timeout in config or simplify model.
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### Expression Not Found
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```
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[ERROR] Expression 'thicknes' not found in model
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```
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**Solution**: Check spelling, verify expression exists in NX.
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### OP2 File Missing
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```
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[ERROR] OP2 file not found: model.op2
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```
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**Solution**: Check NX solve completed. Review NX log file.
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### Database Locked
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```
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[ERROR] Database is locked
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```
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**Solution**: Another process using database. Wait or kill stale process.
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---
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## Stopping and Resuming
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### Graceful Stop
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Press `Ctrl+C` once. Current trial completes, then exits.
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### Force Stop
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Press `Ctrl+C` twice. Immediate exit (may lose current trial).
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### Resume
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```bash
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python run_optimization.py --resume
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```
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Continues from last completed trial. Same study database used.
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---
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## Post-Run Actions
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After optimization completes:
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1. **Check results**:
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```bash
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python -c "import optuna; s=optuna.load_study(...); print(s.best_params)"
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```
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2. **View in dashboard**: `http://localhost:3000`
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3. **Generate report**: See [OP_04_ANALYZE_RESULTS](./OP_04_ANALYZE_RESULTS.md)
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4. **Update STUDY_REPORT.md**: Fill in results template
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---
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## Protocol Integration
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### With Protocol 10 (IMSO)
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If enabled, optimization runs in two phases:
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1. Characterization (10-30 trials)
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2. Optimization (remaining trials)
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Dashboard shows phase transitions.
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### With Protocol 11 (Multi-Objective)
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If 2+ objectives, uses NSGA-II. Returns Pareto front, not single best.
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### With Protocol 13 (Dashboard)
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Writes `optimizer_state.json` every trial for real-time updates.
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### With Protocol 14 (Neural)
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If `--neural` flag, uses trained surrogate for fast evaluation.
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---
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## Troubleshooting
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| Symptom | Cause | Solution |
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|---------|-------|----------|
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| "ModuleNotFoundError" | Wrong environment | `conda activate atomizer` |
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| All trials pruned | Constraints too tight | Relax constraints |
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| Very slow | Model too complex | Simplify mesh, increase timeout |
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| No improvement | Wrong sampler | Try different algorithm |
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| "NX license error" | License unavailable | Check NX license server |
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---
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## Cross-References
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- **Preceded By**: [OP_01_CREATE_STUDY](./OP_01_CREATE_STUDY.md)
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- **Followed By**: [OP_03_MONITOR_PROGRESS](./OP_03_MONITOR_PROGRESS.md), [OP_04_ANALYZE_RESULTS](./OP_04_ANALYZE_RESULTS.md)
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- **Integrates With**: [SYS_10_IMSO](../system/SYS_10_IMSO.md), [SYS_13_DASHBOARD_TRACKING](../system/SYS_13_DASHBOARD_TRACKING.md)
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---
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## Version History
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| Version | Date | Changes |
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|---------|------|---------|
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| 1.0 | 2025-12-05 | Initial release |
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