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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docs/protocols/operations/OP_05_EXPORT_TRAINING_DATA.md
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# OP_05: Export Training Data
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<!--
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PROTOCOL: Export Neural Network Training Data
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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: [SYS_14_NEURAL_ACCELERATION]
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-->
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## Overview
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This protocol covers exporting FEA simulation data for training neural network surrogates. Proper data export enables Protocol 14 (Neural Acceleration).
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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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| "export training data" | Follow this protocol |
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| "neural network data" | Follow this protocol |
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| Planning >50 trials | Consider export for acceleration |
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| Want to train surrogate | Follow this protocol |
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---
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## Quick Reference
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**Export Command**:
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```bash
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python run_optimization.py --export-training
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```
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**Output Structure**:
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```
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atomizer_field_training_data/{study_name}/
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├── trial_0001/
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│ ├── input/model.bdf
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│ ├── output/model.op2
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│ └── metadata.json
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├── trial_0002/
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│ └── ...
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└── study_summary.json
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```
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**Recommended Data Volume**:
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| Complexity | Training Samples | Validation Samples |
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|------------|-----------------|-------------------|
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| Simple (2-3 params) | 50-100 | 20-30 |
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| Medium (4-6 params) | 100-200 | 30-50 |
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| Complex (7+ params) | 200-500 | 50-100 |
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---
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## Configuration
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### Enable Export in Config
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Add to `optimization_config.json`:
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```json
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{
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"training_data_export": {
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"enabled": true,
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"export_dir": "atomizer_field_training_data/my_study",
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"export_bdf": true,
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"export_op2": true,
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"export_fields": ["displacement", "stress"],
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"include_failed": false
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}
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}
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```
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### Configuration Options
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| Option | Type | Default | Description |
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|--------|------|---------|-------------|
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| `enabled` | bool | false | Enable export |
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| `export_dir` | string | - | Output directory |
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| `export_bdf` | bool | true | Save Nastran input |
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| `export_op2` | bool | true | Save binary results |
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| `export_fields` | list | all | Which result fields |
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| `include_failed` | bool | false | Include failed trials |
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---
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## Export Workflow
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### Step 1: Run with Export Enabled
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```bash
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conda activate atomizer
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cd studies/my_study
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python run_optimization.py --export-training
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```
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Or run standard optimization with config export enabled.
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### Step 2: Verify Export
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```bash
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ls atomizer_field_training_data/my_study/
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# Should see trial_0001/, trial_0002/, etc.
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# Check a trial
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ls atomizer_field_training_data/my_study/trial_0001/
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# input/model.bdf
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# output/model.op2
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# metadata.json
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```
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### Step 3: Check Metadata
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```bash
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cat atomizer_field_training_data/my_study/trial_0001/metadata.json
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```
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```json
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{
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"trial_number": 1,
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"design_parameters": {
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"thickness": 5.2,
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"width": 30.0
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},
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"objectives": {
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"mass": 0.234,
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"max_stress": 198.5
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},
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"constraints_satisfied": true,
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"simulation_time": 145.2
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}
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```
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### Step 4: Check Study Summary
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```bash
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cat atomizer_field_training_data/my_study/study_summary.json
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```
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```json
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{
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"study_name": "my_study",
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"total_trials": 50,
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"successful_exports": 47,
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"failed_exports": 3,
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"design_parameters": ["thickness", "width"],
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"objectives": ["mass", "max_stress"],
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"export_timestamp": "2025-12-05T15:30:00"
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}
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```
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---
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## Data Quality Checks
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### Verify Sample Count
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```python
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from pathlib import Path
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import json
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export_dir = Path("atomizer_field_training_data/my_study")
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trials = list(export_dir.glob("trial_*"))
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print(f"Exported trials: {len(trials)}")
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# Check for missing files
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for trial_dir in trials:
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bdf = trial_dir / "input" / "model.bdf"
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op2 = trial_dir / "output" / "model.op2"
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meta = trial_dir / "metadata.json"
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if not all([bdf.exists(), op2.exists(), meta.exists()]):
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print(f"Missing files in {trial_dir}")
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```
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### Check Parameter Coverage
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```python
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import json
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import numpy as np
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# Load all metadata
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params = []
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for trial_dir in export_dir.glob("trial_*"):
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with open(trial_dir / "metadata.json") as f:
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meta = json.load(f)
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params.append(meta["design_parameters"])
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# Check coverage
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import pandas as pd
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df = pd.DataFrame(params)
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print(df.describe())
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# Look for gaps
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for col in df.columns:
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print(f"{col}: min={df[col].min():.2f}, max={df[col].max():.2f}")
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```
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---
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## Space-Filling Sampling
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For best neural network training, use space-filling designs:
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### Latin Hypercube Sampling
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```python
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from scipy.stats import qmc
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# Generate space-filling samples
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n_samples = 100
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n_params = 4
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sampler = qmc.LatinHypercube(d=n_params)
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samples = sampler.random(n=n_samples)
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# Scale to parameter bounds
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lower = [2.0, 20.0, 5.0, 1.0]
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upper = [10.0, 50.0, 15.0, 5.0]
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scaled = qmc.scale(samples, lower, upper)
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```
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### Sobol Sequence
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```python
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sampler = qmc.Sobol(d=n_params)
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samples = sampler.random(n=n_samples)
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scaled = qmc.scale(samples, lower, upper)
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```
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---
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## Next Steps After Export
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### 1. Parse to Neural Format
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```bash
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cd atomizer-field
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python batch_parser.py ../atomizer_field_training_data/my_study
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```
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### 2. Split Train/Validation
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```python
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from sklearn.model_selection import train_test_split
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# 80/20 split
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train_trials, val_trials = train_test_split(
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all_trials,
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test_size=0.2,
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random_state=42
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)
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```
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### 3. Train Model
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```bash
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python train_parametric.py \
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--train_dir ../training_data/parsed \
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--val_dir ../validation_data/parsed \
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--epochs 200
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```
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See [SYS_14_NEURAL_ACCELERATION](../system/SYS_14_NEURAL_ACCELERATION.md) for full training workflow.
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---
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## Troubleshooting
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| Symptom | Cause | Solution |
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|---------|-------|----------|
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| No export directory | Export not enabled | Add `training_data_export` to config |
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| Missing OP2 files | Solve failed | Check `include_failed: false` |
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| Incomplete metadata | Extraction error | Check extractor logs |
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| Low sample count | Too many failures | Relax constraints |
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---
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## Cross-References
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- **Related**: [SYS_14_NEURAL_ACCELERATION](../system/SYS_14_NEURAL_ACCELERATION.md)
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- **Preceded By**: [OP_02_RUN_OPTIMIZATION](./OP_02_RUN_OPTIMIZATION.md)
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- **Skill**: `.claude/skills/modules/neural-acceleration.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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