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/create-study-wizard.md
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# Create Study Wizard Skill
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**Version**: 3.0 - StudyWizard Integration
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**Last Updated**: 2025-12-06
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You are helping the user create a complete Atomizer optimization study using the powerful `StudyWizard` class.
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---
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## Quick Reference
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```python
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from optimization_engine.study_wizard import StudyWizard, create_study, list_extractors
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# Option 1: One-liner for simple studies
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create_study(
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study_name="my_study",
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description="Optimize bracket for stiffness",
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prt_file="path/to/model.prt",
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design_variables=[
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{"parameter": "thickness", "bounds": [5, 20], "units": "mm"}
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],
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objectives=[
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{"name": "stiffness", "goal": "maximize", "extractor": "extract_displacement"}
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],
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constraints=[
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{"name": "mass", "type": "less_than", "threshold": 0.5, "extractor": "extract_mass_from_bdf", "units": "kg"}
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]
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)
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# Option 2: Step-by-step with full control
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wizard = StudyWizard("my_study", "Optimize bracket")
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wizard.set_model_files("path/to/model.prt")
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wizard.introspect() # Discover expressions, solutions
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wizard.add_design_variable("thickness", bounds=(5, 20), units="mm")
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wizard.add_objective("mass", goal="minimize", extractor="extract_mass_from_bdf")
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wizard.add_constraint("stress", type="less_than", threshold=250, extractor="extract_solid_stress", units="MPa")
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wizard.generate()
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```
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---
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## Trigger Phrases
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Use this skill when user says:
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- "create study", "new study", "set up study", "create optimization"
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- "optimize my [part/model/bracket/component]"
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- "help me minimize [mass/weight/cost]"
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- "help me maximize [stiffness/strength/frequency]"
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- "I want to find the best [design/parameters]"
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---
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## Workflow Steps
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### Step 1: Gather Requirements
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Ask the user (if not already provided):
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1. **Model files**: "Where is your NX model? (path to .prt file)"
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2. **Optimization goal**: "What do you want to optimize?"
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- Minimize mass/weight
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- Maximize stiffness
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- Target a specific frequency
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- Multi-objective trade-off
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3. **Constraints**: "What limits must be respected?"
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- Max stress < yield/safety factor
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- Max displacement < tolerance
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- Mass budget
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### Step 2: Introspect Model
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```python
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from optimization_engine.study_wizard import StudyWizard
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wizard = StudyWizard("study_name", "Description")
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wizard.set_model_files("path/to/model.prt")
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result = wizard.introspect()
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# Show user what was found
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print(f"Found {len(result.expressions)} expressions:")
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for expr in result.expressions[:10]:
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print(f" {expr['name']}: {expr.get('value', 'N/A')}")
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print(f"\nFound {len(result.solutions)} solutions:")
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for sol in result.solutions:
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print(f" {sol['name']}")
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# Suggest design variables
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suggestions = result.suggest_design_variables()
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for s in suggestions:
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print(f" {s['name']}: {s['current_value']} -> bounds {s['suggested_bounds']}")
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```
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### Step 3: Configure Study
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```python
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# Add design variables from introspection suggestions
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for dv in selected_design_variables:
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wizard.add_design_variable(
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parameter=dv['name'],
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bounds=dv['bounds'],
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units=dv.get('units', ''),
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description=dv.get('description', '')
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)
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# Add objectives
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wizard.add_objective(
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name="mass",
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goal="minimize",
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extractor="extract_mass_from_bdf",
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description="Minimize total bracket mass"
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)
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wizard.add_objective(
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name="stiffness",
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goal="maximize",
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extractor="extract_displacement",
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params={"invert_for_stiffness": True},
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description="Maximize structural stiffness"
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)
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# Add constraints
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wizard.add_constraint(
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name="max_stress",
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constraint_type="less_than",
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threshold=250,
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extractor="extract_solid_stress",
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units="MPa",
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description="Keep stress below yield/4"
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)
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# Set protocol based on objectives
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if len(wizard.objectives) > 1:
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wizard.set_protocol("protocol_11_multi") # NSGA-II
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else:
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wizard.set_protocol("protocol_10_single") # TPE
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wizard.set_trials(100)
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```
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### Step 4: Generate Study
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```python
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files = wizard.generate()
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print("Study generated successfully!")
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print(f"Location: {wizard.study_dir}")
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print("\nNext steps:")
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print(" 1. cd", wizard.study_dir)
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print(" 2. python run_optimization.py --discover")
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print(" 3. python run_optimization.py --validate")
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print(" 4. python run_optimization.py --run --trials 100")
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```
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---
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## Available Extractors
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| Extractor | What it extracts | Input | Output |
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|-----------|------------------|-------|--------|
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| `extract_mass_from_bdf` | Total mass | .dat/.bdf | kg |
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| `extract_part_mass` | CAD mass | .prt | kg |
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| `extract_displacement` | Max displacement | .op2 | mm |
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| `extract_solid_stress` | Von Mises stress | .op2 | MPa |
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| `extract_principal_stress` | Principal stresses | .op2 | MPa |
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| `extract_strain_energy` | Strain energy | .op2 | J |
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| `extract_spc_forces` | Reaction forces | .op2 | N |
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| `extract_frequency` | Natural frequencies | .op2 | Hz |
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| `get_first_frequency` | First mode frequency | .f06 | Hz |
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| `extract_temperature` | Nodal temperatures | .op2 | K/°C |
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| `extract_modal_mass` | Modal effective mass | .f06 | kg |
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| `extract_zernike_from_op2` | Zernike WFE | .op2+.bdf | nm |
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**List all extractors programmatically**:
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```python
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from optimization_engine.study_wizard import list_extractors
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for name, info in list_extractors().items():
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print(f"{name}: {info['description']}")
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```
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---
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## Common Optimization Patterns
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### Pattern 1: Minimize Mass with Stress Constraint
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```python
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create_study(
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study_name="lightweight_bracket",
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description="Minimize mass while keeping stress below yield",
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prt_file="Bracket.prt",
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design_variables=[
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{"parameter": "wall_thickness", "bounds": [2, 10], "units": "mm"},
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{"parameter": "rib_count", "bounds": [2, 8], "units": "count"}
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],
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objectives=[
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{"name": "mass", "goal": "minimize", "extractor": "extract_mass_from_bdf"}
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],
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constraints=[
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{"name": "stress", "type": "less_than", "threshold": 250,
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"extractor": "extract_solid_stress", "units": "MPa"}
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],
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protocol="protocol_10_single"
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)
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```
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### Pattern 2: Multi-Objective Stiffness vs Mass
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```python
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create_study(
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study_name="pareto_bracket",
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description="Trade-off between stiffness and mass",
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prt_file="Bracket.prt",
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design_variables=[
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{"parameter": "thickness", "bounds": [5, 25], "units": "mm"},
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{"parameter": "support_angle", "bounds": [20, 70], "units": "degrees"}
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],
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objectives=[
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{"name": "stiffness", "goal": "maximize", "extractor": "extract_displacement"},
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{"name": "mass", "goal": "minimize", "extractor": "extract_mass_from_bdf"}
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],
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constraints=[
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{"name": "mass_limit", "type": "less_than", "threshold": 0.5,
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"extractor": "extract_mass_from_bdf", "units": "kg"}
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],
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protocol="protocol_11_multi",
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n_trials=150
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)
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```
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### Pattern 3: Frequency-Targeted Modal Optimization
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```python
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create_study(
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study_name="modal_bracket",
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description="Tune first natural frequency to target",
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prt_file="Bracket.prt",
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design_variables=[
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{"parameter": "thickness", "bounds": [3, 15], "units": "mm"},
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{"parameter": "length", "bounds": [50, 150], "units": "mm"}
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],
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objectives=[
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{"name": "frequency_error", "goal": "minimize",
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"extractor": "get_first_frequency",
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"params": {"target": 100}} # Target 100 Hz
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],
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constraints=[
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{"name": "mass", "type": "less_than", "threshold": 0.3,
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"extractor": "extract_mass_from_bdf", "units": "kg"}
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]
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)
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```
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### Pattern 4: Thermal Optimization
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```python
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create_study(
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study_name="heat_sink",
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description="Minimize max temperature",
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prt_file="HeatSink.prt",
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design_variables=[
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{"parameter": "fin_height", "bounds": [10, 50], "units": "mm"},
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{"parameter": "fin_count", "bounds": [5, 20], "units": "count"}
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],
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objectives=[
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{"name": "max_temp", "goal": "minimize", "extractor": "get_max_temperature"}
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],
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constraints=[
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{"name": "mass", "type": "less_than", "threshold": 0.2,
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"extractor": "extract_mass_from_bdf", "units": "kg"}
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]
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)
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```
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---
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## Protocol Selection Guide
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| Scenario | Protocol | Sampler |
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|----------|----------|---------|
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| Single objective | `protocol_10_single` | TPESampler |
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| Multiple objectives (Pareto) | `protocol_11_multi` | NSGAIISampler |
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| Smooth design space | `protocol_10_single` | CmaEsSampler |
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| Discrete variables | `protocol_10_single` | TPESampler |
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---
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## Files Generated
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The wizard generates a complete study structure:
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```
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studies/{study_name}/
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├── 1_setup/
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│ ├── model/ # NX model files (copied)
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│ ├── optimization_config.json
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│ └── workflow_config.json
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├── 2_results/ # Created on first run
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├── run_optimization.py # Main script with staged workflow
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├── reset_study.py # Reset results
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├── README.md # Engineering documentation
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├── STUDY_REPORT.md # Results tracking template
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└── MODEL_INTROSPECTION.md # Model analysis report
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```
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---
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## Staged Workflow
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After generation, guide user through staged validation:
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```bash
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# Stage 1: Discover model outputs
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python run_optimization.py --discover
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# Stage 2: Validate with single trial
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python run_optimization.py --validate
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# Stage 3: Integration test (3 trials)
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python run_optimization.py --test
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# Stage 4: Full optimization
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python run_optimization.py --run --trials 100
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# Resume if interrupted
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python run_optimization.py --run --trials 50 --resume
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```
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---
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## Error Handling
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### Common Issues and Solutions
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| Error | Cause | Solution |
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|-------|-------|----------|
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| "Part file not found" | Wrong path | Use absolute path or relative to studies/ |
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| "No expressions found" | Part not parametric | Add expressions in NX Modeling |
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| "Simulation failed" | NX not running | Start NX before running |
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| "Extractor not found" | Typo in name | Use `list_extractors()` to see valid names |
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---
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## Example Conversation
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**User**: "I want to optimize my bracket for minimum weight while keeping stress low"
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**Claude**: "I'll help you create a weight optimization study. Let me gather some information:
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1. Where is your NX model file?
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2. What's the maximum allowable stress (e.g., yield/4)?
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3. Are there specific parameters you want to vary (thickness, angles, etc.)?"
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**User**: "The model is at studies/my_bracket/model/Bracket.prt. Max stress should be 250 MPa. I want to vary the wall_thickness and support_angle."
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**Claude**: "I'll create the study and introspect your model to confirm the parameters exist."
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```python
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from optimization_engine.study_wizard import StudyWizard
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wizard = StudyWizard(
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study_name="my_bracket_optimization",
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description="Minimize bracket mass with stress constraint"
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)
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wizard.set_model_files("studies/my_bracket/model/Bracket.prt")
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# Introspect to verify parameters
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result = wizard.introspect()
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print(f"Found expressions: {result.get_expression_names()}")
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# Configure
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wizard.add_design_variable("wall_thickness", bounds=(2, 15), units="mm")
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wizard.add_design_variable("support_angle", bounds=(20, 60), units="degrees")
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wizard.add_objective("mass", goal="minimize", extractor="extract_mass_from_bdf")
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wizard.add_constraint("stress", constraint_type="less_than", threshold=250,
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extractor="extract_solid_stress", units="MPa")
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wizard.set_trials(100)
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files = wizard.generate()
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```
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**Claude**: "Study created! Here's what I generated:
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- `studies/my_bracket_optimization/run_optimization.py`
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- `studies/my_bracket_optimization/README.md`
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- Configuration files in `1_setup/`
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Next steps:
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1. `cd studies/my_bracket_optimization`
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2. `python run_optimization.py --discover`
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3. `python run_optimization.py --validate`
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4. `python run_optimization.py --run --trials 100`"
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---
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## Cross-References
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- **Extractor Library**: `docs/protocols/system/SYS_12_EXTRACTOR_LIBRARY.md`
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- **Protocol 10 (IMSO)**: `docs/protocols/system/SYS_10_IMSO.md`
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- **Protocol 11 (Multi-Objective)**: `docs/protocols/system/SYS_11_MULTI_OBJECTIVE.md`
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- **StudyWizard Source**: `optimization_engine/study_wizard.py`
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Reference in New Issue
Block a user