602560c46a
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>
61 lines
983 B
Markdown
61 lines
983 B
Markdown
# Study Report: bracket_pareto_3obj
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**Status**: Not Started
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**Created**: 2025-12-06 14:43
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**Last Updated**: 2025-12-06 14:43
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---
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## 1. Optimization Progress
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| Metric | Value |
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|--------|-------|
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| Total Trials | 0 |
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| Successful Trials | 0 |
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| Best Objective | - |
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| Duration | - |
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## 2. Best Solutions
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*No optimization runs completed yet.*
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## 3. Pareto Front (if multi-objective)
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*No Pareto front generated yet.*
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## 4. Design Variable Sensitivity
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*Analysis pending optimization runs.*
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## 5. Constraint Satisfaction
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*Analysis pending optimization runs.*
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## 6. Recommendations
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*Recommendations will be added after optimization runs.*
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---
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## 7. Next Steps
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1. [ ] Run `python run_optimization.py --discover`
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2. [ ] Run `python run_optimization.py --validate`
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3. [ ] Run `python run_optimization.py --test`
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4. [ ] Run `python run_optimization.py --run --trials 100`
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5. [ ] Analyze results and update this report
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---
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*Generated by StudyWizard*
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