Files

Antoine 602560c46a 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>

2025-12-06 20:01:59 -05:00

983 B

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Study Report: bracket_pareto_3obj

Status: Not Started Created: 2025-12-06 14:43 Last Updated: 2025-12-06 14:43

1. Optimization Progress

Metric	Value
Total Trials	0
Successful Trials	0
Best Objective	-
Duration	-

2. Best Solutions

No optimization runs completed yet.

3. Pareto Front (if multi-objective)

No Pareto front generated yet.

4. Design Variable Sensitivity

Analysis pending optimization runs.

5. Constraint Satisfaction

Analysis pending optimization runs.

6. Recommendations

Recommendations will be added after optimization runs.

7. Next Steps

Run python run_optimization.py --discover
Run python run_optimization.py --validate
Run python run_optimization.py --test
Run python run_optimization.py --run --trials 100
Analyze results and update this report

Generated by StudyWizard

983 B Raw Blame History