Antoine/Atomizer
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity
90 Commits 3 Branches 1 Tag
3e9488d9f013169d2e52c5fb1e0e9b14c9a42eb8
Commit Graph

3 Commits

Author SHA1 Message Date
Antoine
3e9488d9f0 feat: Add Adaptive Method Selector for intelligent optimization strategy 
The AMS analyzes optimization problems and recommends the best method:
- ProblemProfiler: Static analysis of config (dimensions, objectives, constraints)
- EarlyMetricsCollector: Dynamic analysis from FEA trials (smoothness, correlations)
- AdaptiveMethodSelector: Rule-based scoring for method recommendations
- RuntimeAdvisor: Mid-run monitoring for method pivots

Key features:
- Analyzes problem characteristics (n_variables, n_objectives, constraints)
- Computes response smoothness and variable sensitivity from trial data
- Recommends TURBO, HYBRID_LOOP, PURE_FEA, or GNN_FIELD
- Provides confidence scores and suggested parameters
- CLI: python -m optimization_engine.method_selector <config> [db]

Documentation:
- Add SYS_15_METHOD_SELECTOR.md protocol
- Update CLAUDE.md with new system protocol reference

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
 2025-12-07 05:51:49 -05:00
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
Antoine
0cb2808c44 feat: Add Phase 2 & 3 physics extractors for multi-physics optimization 
Phase 2 - Structural Analysis:
- extract_principal_stress: σ1, σ2, σ3 principal stresses from OP2
- extract_strain_energy: Element and total strain energy
- extract_spc_forces: Reaction forces at boundary conditions

Phase 3 - Multi-Physics:
- extract_temperature: Nodal temperatures from thermal OP2 (SOL 153/159)
- extract_temperature_gradient: Thermal gradient approximation
- extract_heat_flux: Element heat flux from thermal analysis
- extract_modal_mass: Modal effective mass from F06 (SOL 103)
- get_first_frequency: Convenience function for first natural frequency

Documentation:
- Updated SYS_12_EXTRACTOR_LIBRARY.md with E12-E18 specifications
- Updated NX_OPEN_AUTOMATION_ROADMAP.md marking Phase 3 complete
- Added test_phase3_extractors.py for validation

All extractors follow consistent API pattern returning Dict with
success, data, and error fields for robust error handling.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
 2025-12-06 13:40:14 -05:00