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40 Commits

Author SHA1 Message Date
Anto01
abdbe9a708 fix: correct all baseline values from actual SAT3 model expression export 
Previous baselines were from old V15 study, not from M1_Tensor best design.
Updated all 9 design variables with correct values from model introspection.

Baseline Corrections (from expression export):
- lateral_inner_angle: 26.79° → 30.18° (at upper bound)
- lateral_outer_angle: 14.64° → 15.09°
- lateral_outer_pivot: 5.5mm → 6.036mm (0.4 × 15.09°)
- lateral_inner_pivot: 10.07mm → 12.072mm (0.4 × 30.18°)
- lateral_middle_pivot: 20.73mm → 14.0mm (lower than expected)
- lateral_closeness: 11.02mm → 7.89mm
- whiffle_min: 40.55mm → 56.7mm
- inner_circular_rib_dia: 534.00mm → 537.86mm (fixed parameter)

Bound Adjustments:
- lateral_inner_pivot max: 11.0 → 13.0mm (to accommodate baseline 12.072)
- lateral_closeness min: 9.5 → 5.0mm (to accommodate baseline 7.89)

Root Cause:
- NX introspection failed (NX not running)
- Config was created with V15 study baselines as placeholders
- Actual model values now applied from user-provided expression export

Files Updated:
- optimization_config.json: All baselines corrected
- README.md: Design variable table updated
- STUDY_REPORT.md: Baseline values corrected

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
 2026-01-29 12:23:43 -05:00
Anto01
b62605a736 refactor: update SAT3_Trajectory to 9 design variables with refined bounds 
Updated configuration based on user adjustments:
- Reduced from 11 to 9 design variables (disabled blank_backface_angle and inner_circular_rib_dia)
- Refined parameter bounds for lateral supports

Design Variable Changes:
- lateral_inner_angle: min 20.0° (was 25.0°)
- lateral_outer_pivot: range 4.0-9.0mm, baseline 5.5mm (was 9.0-12.0mm, baseline 10.40mm)
- lateral_middle_pivot: range 12.0-25.0mm (was 15.0-27.0mm)
- blank_backface_angle: disabled (fixed at 4.00°)
- inner_circular_rib_dia: disabled (fixed at 534.00mm)

Documentation Updated:
- README.md: Updated design variable table with correct ranges and baselines
- STUDY_REPORT.md: Updated to reflect 9 enabled variables
- optimization_config.json: User-modified bounds applied

Rationale:
- Focus optimization on lateral supports and whiffle tree
- Fix geometry parameters to reduce search space
- Tighter bounds on critical lateral parameters

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
 2026-01-29 12:20:41 -05:00
Anto01
f80b5d64a8 feat: create SAT3_Trajectory study with Zernike Trajectory Method 
First production implementation of trajectory-based optimization for M1 mirror.

Study Configuration:
- Optimizer: TPE (100 trials, 15 startup)
- Primary objective: total_filtered_rms_nm (integrated RMS across 20-60 deg)
- Logged objectives: coma_rms_nm, astigmatism_rms_nm, trefoil_rms_nm, spherical_rms_nm
- Design variables: 11 (full wiffle tree + lateral supports)
- Physics validation: R² fit quality monitoring

Key Features:
- Mode-specific aberration tracking (coma, astigmatism, trefoil, spherical)
- Physics-based trajectory model: c_j(θ) = a_j·sin(θ) + b_j·cos(θ)
- Sensitivity analysis: axial vs lateral load contributions
- OPD correction with focal_length=22000mm
- Annular aperture (inner_radius=135.75mm)

Validation Results:
- Tested on existing M1_Tensor OP2: R²=1.0000 (perfect fit)
- Baseline total RMS: 4.30 nm
- All 5 angles auto-detected: [20, 30, 40, 50, 60] deg
- Dominant mode: spherical (10.51 nm)

Files Created:
- studies/M1_Mirror/SAT3_Trajectory/README.md (complete documentation)
- studies/M1_Mirror/SAT3_Trajectory/STUDY_REPORT.md (results template)
- studies/M1_Mirror/SAT3_Trajectory/run_optimization.py (TPE + trajectory extraction)
- studies/M1_Mirror/SAT3_Trajectory/1_setup/optimization_config.json (TPE config)
- studies/M1_Mirror/SAT3_Trajectory/1_setup/model/ (all NX files copied from M1_Tensor)
- test_trajectory_extractor.py (validation script)

References:
- Physics: docs/physics/ZERNIKE_TRAJECTORY_METHOD.md
- Handoff: docs/handoff/SETUP_TRAJECTORY_OPTIMIZATION.md
- Extractor: optimization_engine/extractors/extract_zernike_trajectory.py

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
 2026-01-29 12:10:02 -05:00
Anto01
a26914bbe8 feat: Add Studio UI, intake system, and extractor improvements 
Dashboard:
- Add Studio page with drag-drop model upload and Claude chat
- Add intake system for study creation workflow
- Improve session manager and context builder
- Add intake API routes and frontend components

Optimization Engine:
- Add CLI module for command-line operations
- Add intake module for study preprocessing
- Add validation module with gate checks
- Improve Zernike extractor documentation
- Update spec models with better validation
- Enhance solve_simulation robustness

Documentation:
- Add ATOMIZER_STUDIO.md planning doc
- Add ATOMIZER_UX_SYSTEM.md for UX patterns
- Update extractor library docs
- Add study-readme-generator skill

Tools:
- Add test scripts for extraction validation
- Add Zernike recentering test

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
 2026-01-27 12:02:30 -05:00
Anto01
a3f18dc377 chore: Project cleanup and Canvas UX improvements (Phase 7-9) 
## Cleanup (v0.5.0)
- Delete 102+ orphaned MCP session temp files
- Remove build artifacts (htmlcov, dist, __pycache__)
- Archive superseded plan docs (RALPH_LOOP V2/V3, CANVAS V3, etc.)
- Move debug/analysis scripts from tests/ to tools/analysis/
- Archive redundant NX journals to archive/nx_journals/
- Archive monolithic PROTOCOL.md to docs/archive/
- Update .gitignore with missing patterns
- Clean old study files (optimization_log_old.txt, run_optimization_old.py)

## Canvas UX (Phases 7-9)
- Phase 7: Resizable panels with localStorage persistence
  - Left sidebar: 200-400px, Right panel: 280-600px
  - New useResizablePanel hook and ResizeHandle component
- Phase 8: Enable all palette items
  - All 8 node types now draggable
  - Singleton logic for model/solver/algorithm/surrogate
- Phase 9: Solver configuration
  - Add SolverEngine type (nxnastran, mscnastran, python, etc.)
  - Add NastranSolutionType (SOL101-SOL200)
  - Engine/solution dropdowns in config panel
  - Python script path support

## Documentation
- Update CHANGELOG.md with recent versions
- Update docs/00_INDEX.md
- Create examples/README.md
- Add docs/plans/CANVAS_UX_IMPROVEMENTS.md
 2026-01-24 15:17:34 -05:00
Anto01
5c419e2358 fix(canvas): Multiple fixes for drag-drop, undo/redo, and code generation 
Drag-drop fixes:
- Fix Objective default data: use nested 'source' object with extractor_id/output_name
- Fix Constraint default data: use 'type' field (not constraint_type), 'threshold' (not limit)

Undo/Redo fixes:
- Remove dependency on isDirty flag (which is always false due to auto-save)
- Record snapshots based on actual spec changes via deep comparison

Code generation improvements:
- Update system prompt to support multiple extractor types:
  * OP2-based extractors for FEA results (stress, displacement, frequency)
  * Expression-based extractors for NX model values (dimensions, volumes)
  * Computed extractors for derived values (no FEA needed)
- Claude will now choose appropriate signature based on user's description
 2026-01-20 15:08:49 -05:00
Anto01
2f0f45de86 fix(spec): Correct volume extractor structure in m1_mirror_cost_reduction_lateral 
- Change custom_function.code to function.source_code per AtomizerSpec v2.0 schema
 2026-01-20 14:14:20 -05:00
Anto01
cb6b130908 feat(config): Add AtomizerSpec v2.0 schema and migrate all studies 
Added JSON Schema:
- optimization_engine/schemas/atomizer_spec_v2.json

Migrated 28 studies to AtomizerSpec v2.0 format:
- Drone_Gimbal studies (1)
- M1_Mirror studies (21)
- M2_Mirror studies (2)
- SheetMetal_Bracket studies (4)

Each atomizer_spec.json is the unified configuration containing:
- Design variables with bounds and expressions
- Extractors (standard and custom)
- Objectives and constraints
- Optimization algorithm settings
- Canvas layout information
 2026-01-20 13:11:23 -05:00
Anto01
ea437d360e docs: Major documentation overhaul - restructure folders, update tagline, add Getting Started guide 
- Restructure docs/ folder (remove numeric prefixes):
  - 04_USER_GUIDES -> guides/
  - 05_API_REFERENCE -> api/
  - 06_PHYSICS -> physics/
  - 07_DEVELOPMENT -> development/
  - 08_ARCHIVE -> archive/
  - 09_DIAGRAMS -> diagrams/

- Replace tagline 'Talk, don't click' with 'LLM-driven optimization framework' in 9 files

- Create comprehensive docs/GETTING_STARTED.md:
  - Prerequisites and quick setup
  - Project structure overview
  - First study tutorial (Claude or manual)
  - Dashboard usage guide
  - Neural acceleration introduction

- Rewrite docs/00_INDEX.md with correct paths and modern structure

- Archive obsolete files:
  - 01_PROTOCOLS.md -> archive/historical/01_PROTOCOLS_legacy.md
  - 03_GETTING_STARTED.md -> archive/historical/
  - ATOMIZER_PODCAST_BRIEFING.md -> archive/marketing/

- Update timestamps to 2026-01-20 across all key files

- Update .gitignore to exclude docs/generated/

- Version bump: ATOMIZER_CONTEXT v1.8 -> v2.0
 2026-01-20 10:03:45 -05:00
Anto01
73a7b9d9f1 feat: Add dashboard chat integration and MCP server 
Major changes:
- Dashboard: WebSocket-based chat with session management
- Dashboard: New chat components (ChatPane, ChatInput, ModeToggle)
- Dashboard: Enhanced UI with parallel coordinates chart
- MCP Server: New atomizer-tools server for Claude integration
- Extractors: Enhanced Zernike OPD extractor
- Reports: Improved report generator

New studies (configs and scripts only):
- M1 Mirror: Cost reduction campaign studies
- Simple Beam, Simple Bracket, UAV Arm studies

Note: Large iteration data (2_iterations/, best_design_archive/)
excluded via .gitignore - kept on local Gitea only.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
 2026-01-13 15:53:55 -05:00
Anto01
b1ffc64407 feat: Implement SAT v3 achieving WS=205.58 (new campaign record) 
Self-Aware Turbo v3 optimization validated on M1 Mirror flat back:
- Best WS: 205.58 (12% better than previous best 218.26)
- 100% feasibility rate, 100% unique designs
- Uses 556 training samples from V5-V8 campaign data

Key innovations in V9:
- Adaptive exploration schedule (15% → 8% → 3%)
- Mass threshold at 118 kg (optimal sweet spot)
- 70% exploitation near best design
- Seeded with best known design from V7
- Ensemble surrogate with R²=0.99

Updated documentation:
- SYS_16: SAT protocol updated to v3.0 VALIDATED
- Cheatsheet: Added SAT v3 as recommended method
- Context: Updated protocol overview

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

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
 2025-12-31 16:06:33 -05:00
Anto01
f0e594570a docs: Add comprehensive podcast briefing document 
- Add ATOMIZER_PODCAST_BRIEFING.md with complete technical overview
- Covers all 12 sections: architecture, optimization, neural acceleration
- Includes impressive statistics and metrics for podcast generation
- Update LAC failure insights from recent sessions
- Add M1_Mirror studies README

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

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
 2025-12-30 09:36:40 -05:00
Anto01
faa7779a43 feat: Add L-BFGS gradient optimizer for surrogate polish phase 
Implements gradient-based optimization exploiting MLP surrogate differentiability.
Achieves 100-1000x faster convergence than derivative-free methods (TPE, CMA-ES).

New files:
- optimization_engine/gradient_optimizer.py: GradientOptimizer class with L-BFGS/Adam/SGD
- studies/M1_Mirror/m1_mirror_adaptive_V14/run_lbfgs_polish.py: Per-study runner

Updated docs:
- SYS_14_NEURAL_ACCELERATION.md: Full L-BFGS section (v2.4)
- 01_CHEATSHEET.md: Quick reference for L-BFGS usage
- atomizer_fast_solver_technologies.md: Architecture context

Usage: python -m optimization_engine.gradient_optimizer studies/my_study --n-starts 20

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

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
 2025-12-28 16:36:18 -05:00
Anto01
9aa5f6eb8c chore: Clean up deprecated study folders 
Remove old study folders that have been superseded or archived:
- bracket_pareto_3obj
- bracket_stiffness_optimization (V1-V3)
- bracket_stiffness_optimization_atomizerfield
- drone_gimbal_arm_optimization
- m1_mirror_adaptive_V11 through V15
- m1_mirror_zernike_optimization
- simple_beam_optimization
- training_data_export_test
- uav_arm_atomizerfield_test

These studies have been consolidated or are no longer needed.

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

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
 2025-12-20 13:49:11 -05:00
Antoine
01a7d7d121 docs: Complete M1 mirror optimization campaign V11-V15 
## M1 Mirror Campaign Summary
- V11-V15 optimization campaign completed (~1,400 FEA evaluations)
- Best design: V14 Trial #725 with Weighted Sum = 121.72
- V15 NSGA-II confirmed V14 TPE found optimal solution
- Campaign improved from WS=129.33 (V11) to WS=121.72 (V14): -5.9%

## Key Results
- 40° tracking: 5.99 nm (target 4.0 nm)
- 60° tracking: 13.10 nm (target 10.0 nm)
- Manufacturing: 26.28 nm (target 20.0 nm)
- Targets not achievable within current design space

## Documentation Added
- V15 STUDY_REPORT.md: Detailed NSGA-II results analysis
- M1_MIRROR_CAMPAIGN_SUMMARY.md: Full V11-V15 campaign overview
- Updated CLAUDE.md, ATOMIZER_CONTEXT.md with NXSolver patterns
- Updated 01_CHEATSHEET.md with --resume guidance
- Updated OP_01_CREATE_STUDY.md with FEARunner template

## Studies Added
- m1_mirror_adaptive_V13: TPE validation (291 trials)
- m1_mirror_adaptive_V14: TPE intensive (785 trials, BEST)
- m1_mirror_adaptive_V15: NSGA-II exploration (126 new FEA)

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

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
 2025-12-16 14:55:23 -05:00
Antoine
1bb201e0b7 feat: Add post-optimization tools and mandatory best design archiving 
New Tools (tools/):
- analyze_study.py: Generate comprehensive optimization reports
- find_best_iteration.py: Find best iteration folder, optionally copy it
- archive_best_design.py: Archive best design to 3_results/best_design_archive/<timestamp>/

Protocol Updates:
- OP_02_RUN_OPTIMIZATION.md v1.1: Add mandatory archive_best_design step
  in Post-Run Actions. This MUST be done after every optimization run.

V14 Updates:
- run_optimization.py: Auto-archive best design at end of optimization
- optimization_config.json: Expand bounds for V14 continuation
  - lateral_outer_angle: min 13->11 deg (was at 4.7%)
  - lateral_inner_pivot: min 7->5 mm (was at 8.1%)
  - lateral_middle_pivot: max 23->27 mm (was at 99.4%)
  - whiffle_min: max 60->72 mm (was at 96.3%)

Usage:
  python tools/analyze_study.py m1_mirror_adaptive_V14
  python tools/find_best_iteration.py m1_mirror_adaptive_V14
  python tools/archive_best_design.py m1_mirror_adaptive_V14

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

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
 2025-12-12 10:28:35 -05:00
Antoine
70ac34e3d3 feat: Add E11 Part Mass extractor, document pyNastran mass accuracy issue 
New E11 Part Mass Extractor:
- Add nx_journals/extract_part_mass_material.py - NX journal using
  NXOpen.MeasureManager.NewMassProperties() for accurate geometry-based mass
- Add optimization_engine/extractors/extract_part_mass_material.py - Python
  wrapper that reads JSON output from journal
- Add E11 entry to extractors/catalog.json

Documentation Updates:
- SYS_12_EXTRACTOR_LIBRARY.md: Add mass accuracy warning noting pyNastran
  get_mass_breakdown() under-reports ~7% on hex-dominant meshes with
  tet/pyramid fill elements. E11 (geometry .prt) should be preferred over
  E4 (BDF) unless material is overridden at FEM level.
- 01_CHEATSHEET.md: Add mass extraction tip

V14 Config:
- Expand design variable bounds (blank_backface_angle max 4.5°,
  whiffle_triangle_closeness max 80mm, whiffle_min max 60mm)

Testing showed:
- E11 from .prt: 97.66 kg (accurate - matches NX GUI)
- E4 pyNastran get_mass_breakdown(): 90.73 kg (~7% under-reported)

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

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
 2025-12-11 22:15:36 -05:00
Antoine
3d90097b2b feat: Expand V14 design space bounds based on V13 analysis 
Analysis of 258 FEA trials showed best trial (#45) hitting bounds on
5 parameters. Expanded bounds to allow exploration of promising regions:

- lateral_inner_angle: max 28.5 → 30.0° (was at 99.2% of range)
- lateral_inner_pivot: min 9.0 → 7.0 mm (was at 4.6% of range)
- lateral_middle_pivot: min 18.0 → 15.0 mm (was at 7.7% of range)
- whiffle_min: min 35.0 → 30.0 mm (was at 4.0% of range)
- whiffle_outer_to_vertical: min 68.0 → 60.0° (was at 5.3% of range)
- blank_backface_angle: narrowed to 4.1-4.2° (focus on optimal region)

V14 seeds from 496 prior FEA trials (V11+V12+V13) using TPE sampler.

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

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
 2025-12-11 16:42:34 -05:00
Antoine
48404fd743 feat: Add Zernike wavefront viewer and V14 TPE optimization study 
Dashboard Zernike Analysis:
- Add ZernikeViewer component with tabbed UI (40°, 60°, 90° vs 20°)
- Generate 3D surface mesh plots with Mesh3d triangulation
- Full 50-mode Zernike coefficient tables with mode names
- Manufacturing metrics for 90_vs_20 (optician workload analysis)
- OP2 availability filter for FEA trials only
- Fix duplicate trial display with unique React keys
- Tab switching with proper event propagation

Backend API Enhancements:
- GET /studies/{id}/trials/{num}/zernike - Generate Zernike HTML on-demand
- GET /studies/{id}/zernike-available - List trials with OP2 files
- compute_manufacturing_metrics() for aberration analysis
- compute_rms_filter_j1to3() for optician workload metric

M1 Mirror V14 Study:
- TPE (Tree-structured Parzen Estimator) optimization
- Seeds from 496 prior FEA trials (V11+V12+V13)
- Weighted-sum objective: 5*obj_40 + 5*obj_60 + 1*obj_mfg
- Multivariate TPE with constant_liar for efficient exploration
- Ready for 8-hour overnight runs

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

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
 2025-12-10 21:34:07 -05:00
Antoine
96b196de58 feat: Add Zernike GNN surrogate module and M1 mirror V12/V13 studies 
This commit introduces the GNN-based surrogate for Zernike mirror optimization
and the M1 mirror study progression from V12 (GNN validation) to V13 (pure NSGA-II).

## GNN Surrogate Module (optimization_engine/gnn/)

New module for Graph Neural Network surrogate prediction of mirror deformations:

- `polar_graph.py`: PolarMirrorGraph - fixed 3000-node polar grid structure
- `zernike_gnn.py`: ZernikeGNN with design-conditioned message passing
- `differentiable_zernike.py`: GPU-accelerated Zernike fitting and objectives
- `train_zernike_gnn.py`: ZernikeGNNTrainer with multi-task loss
- `gnn_optimizer.py`: ZernikeGNNOptimizer for turbo mode (~900k trials/hour)
- `extract_displacement_field.py`: OP2 to HDF5 field extraction
- `backfill_field_data.py`: Extract fields from existing FEA trials

Key innovation: Design-conditioned convolutions that modulate message passing
based on structural design parameters, enabling accurate field prediction.

## M1 Mirror Studies

### V12: GNN Field Prediction + FEA Validation
- Zernike GNN trained on V10/V11 FEA data (238 samples)
- Turbo mode: 5000 GNN predictions → top candidates → FEA validation
- Calibration workflow for GNN-to-FEA error correction
- Scripts: run_gnn_turbo.py, validate_gnn_best.py, compute_full_calibration.py

### V13: Pure NSGA-II FEA (Ground Truth)
- Seeds 217 FEA trials from V11+V12
- Pure multi-objective NSGA-II without any surrogate
- Establishes ground-truth Pareto front for GNN accuracy evaluation
- Narrowed blank_backface_angle range to [4.0, 5.0]

## Documentation Updates

- SYS_14: Added Zernike GNN section with architecture diagrams
- CLAUDE.md: Added GNN module reference and quick start
- V13 README: Study documentation with seeding strategy

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

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
 2025-12-10 08:44:04 -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
f8b90156b3 feat: Improve dashboard performance and Claude terminal context 
- Add trial limiting (300 max) and reduce polling to 15s for large studies
- Make dashboard layout wider with col-span adjustments
- Claude terminal now runs from Atomizer root for CLAUDE.md/skills access
- Add study context display in terminal on connect
- Add KaTeX math rendering styles for study reports
- Add surrogate tuner module for hyperparameter optimization
- Fix backend proxy to port 8001

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

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-12-04 17:36:00 -05:00
Antoine
8cbdbcad78 feat: Add Protocol 13 adaptive optimization, Plotly charts, and dashboard improvements 
## Protocol 13: Adaptive Multi-Objective Optimization
- Iterative FEA + Neural Network surrogate workflow
- Initial FEA sampling, NN training, NN-accelerated search
- FEA validation of top NN predictions, retraining loop
- adaptive_state.json tracks iteration history and best values
- M1 mirror study (V11) with 103 FEA, 3000 NN trials

## Dashboard Visualization Enhancements
- Added Plotly.js interactive charts (parallel coords, Pareto, convergence)
- Lazy loading with React.lazy() for performance
- Code splitting: plotly.js-basic-dist (~1MB vs 3.5MB)
- Chart library toggle (Recharts default, Plotly on-demand)
- ExpandableChart component for full-screen modal views
- ConsoleOutput component for real-time log viewing

## Documentation
- Protocol 13 detailed documentation
- Dashboard visualization guide
- Plotly components README
- Updated run-optimization skill with Mode 5 (adaptive)

## Bug Fixes
- Fixed TypeScript errors in dashboard components
- Fixed Card component to accept ReactNode title
- Removed unused imports across components

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

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-12-04 07:41:54 -05:00
Antoine
ec5e42d733 feat: Add M1 mirror Zernike optimization with correct RMS calculation 
Major improvements to telescope mirror optimization workflow:

Assembly FEM Workflow (solve_simulation.py):
- Fixed multi-part assembly FEM update sequence
- Use ImportFromFile() for reliable expression updates
- Add DuplicateNodesCheckBuilder with MergeOccurrenceNodes=True
- Switch to Foreground solve mode for multi-subcase solutions
- Add detailed logging and diagnostics for node merge operations

Zernike RMS Calculation:
- CRITICAL FIX: Use correct surface-based RMS formula
  - Global RMS = sqrt(mean(W^2)) from actual WFE values
  - Filtered RMS = sqrt(mean(W_residual^2)) after removing low-order fit
  - This matches zernike_Post_Script_NX.py (optical standard)
- Previous WRONG formula was: sqrt(sum(coeffs^2))
- Add compute_rms_filter_j1to3() for optician workload metric

Subcase Mapping:
- Fix subcase mapping to match NX model:
  - Subcase 1 = 90 deg (polishing orientation)
  - Subcase 2 = 20 deg (reference)
  - Subcase 3 = 40 deg
  - Subcase 4 = 60 deg

New Study: M1 Mirror Zernike Optimization
- Full optimization config with 11 design variables
- 3 objectives: rel_filtered_rms_40_vs_20, rel_filtered_rms_60_vs_20, mfg_90_optician_workload
- Neural surrogate support for accelerated optimization

Documentation:
- Update ZERNIKE_INTEGRATION.md with correct RMS formula
- Update ASSEMBLY_FEM_WORKFLOW.md with expression import and node merge details
- Add reference scripts from original zernike_Post_Script_NX.py

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

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-11-28 16:30:15 -05:00
Anto01
a4805947d1 feat: Add NX study models and optimization histories 
Includes all study folders with NX models for development:
- bracket_stiffness_optimization (V1, V2, V3)
- drone_gimbal_arm_optimization
- simple_beam_optimization
- uav_arm_optimization (V1, V2)
- training_data_export_test
- uav_arm_atomizerfield_test

Contains .prt, .fem, .sim files and optimization databases.

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

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-11-26 12:19:07 -05:00
Anto01
2b3573ec42 feat: Add AtomizerField training data export and intelligent model discovery 
Major additions:
- Training data export system for AtomizerField neural network training
- Bracket stiffness optimization study with 50+ training samples
- Intelligent NX model discovery (auto-detect solutions, expressions, mesh)
- Result extractors module for displacement, stress, frequency, mass
- User-generated NX journals for advanced workflows
- Archive structure for legacy scripts and test outputs
- Protocol documentation and dashboard launcher

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

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-11-26 12:01:50 -05:00
Anto01
a0c008a593 feat: Add neural loop automation - templates, auto-trainer, CLI 
Closes the neural training loop with automated workflow:
- atomizer.py: One-command neural workflow CLI
- auto_trainer.py: Auto-training trigger system (50pt threshold)
- template_loader.py: Study creation from templates
- study_reset.py: Study reset/cleanup utility
- 3 templates: beam stiffness, bracket stress, frequency tuning
- State assessment document (Nov 25)

Usage: python atomizer.py neural-optimize --study my_study --trials 500

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

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-11-26 07:53:00 -05:00
Anto01
e3bdb08a22 feat: Major update with validators, skills, dashboard, and docs reorganization 
- Add validation framework (config, model, results, study validators)
- Add Claude Code skills (create-study, run-optimization, generate-report,
  troubleshoot, analyze-model)
- Add Atomizer Dashboard (React frontend + FastAPI backend)
- Reorganize docs into structured directories (00-09)
- Add neural surrogate modules and training infrastructure
- Add multi-objective optimization support

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

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-11-25 19:23:58 -05:00
Anto01
7837255ba8 feat: Update create-study skill with Phase 1.3 logging and create UAV arm test study 
Phase 1.3.1 Complete - Logging Integration:

1. Updated .claude/skills/create-study.md:
   - Added IMPORTANT section on structured logging from Phase 1.3
   - Documents logger import and initialization
   - Lists all structured logging methods (trial_start, trial_complete, etc.)
   - References drone_gimbal_arm as template

2. Created studies/uav_arm_optimization/:
   - Multi-objective NSGA-II study (50 trials)
   - Same type as drone_gimbal_arm but renamed for UAV context
   - Full integration with Phase 1.3 logging system
   - Configuration: minimize mass + maximize frequency
   - Running to validate complete logging system

Benefits:
- All future studies created via skill will have consistent logging
- Production-ready error handling and file logging from day 1
- Color-coded console output for better monitoring
- Automatic log rotation (50MB, 3 backups)

Related: Phase 1.2 (Configuration), Phase 1.3 (Logger), Phase 1.3.1 (Integration)

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

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-11-24 10:18:20 -05:00
Anto01
d2c18bb7db feat: Migrate drone_gimbal_arm_optimization to use structured logging system (Phase 1.3.1) 
Migrate drone_gimbal_arm study as reference implementation for Phase 1.3 logging system.

Changes:
- Replace all print() statements with logger calls throughout run_optimization.py
- Add logger.trial_start() and logger.trial_complete() for structured trial logging
- Use logger.trial_failed() for error handling with full tracebacks
- Add logger.study_start() and logger.study_complete() for lifecycle logging
- Replace constraint violation prints with logger.warning()
- Create comprehensive LOGGING_MIGRATION_GUIDE.md with before/after examples

Benefits:
- Color-coded console output (green INFO, yellow WARNING, red ERROR)
- Automatic file logging to 2_results/optimization.log with rotation (50MB, 3 backups)
- Structured format with timestamps for dashboard integration
- Professional error handling with exc_info=True
- Reference implementation for migrating remaining studies

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

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-11-24 09:39:56 -05:00
Anto01
d228ccec66 refactor: Archive experimental LLM features for MVP stability (Phase 1.1) 
Moved experimental LLM integration code to optimization_engine/future/:
- llm_optimization_runner.py - Runtime LLM API runner
- llm_workflow_analyzer.py - Workflow analysis
- inline_code_generator.py - Auto-generate calculations
- hook_generator.py - Auto-generate hooks
- report_generator.py - LLM report generation
- extractor_orchestrator.py - Extractor orchestration

Added comprehensive optimization_engine/future/README.md explaining:
- MVP LLM strategy (Claude Code skills, not runtime LLM)
- Why files were archived
- When to revisit post-MVP
- Production architecture reference

Production runner confirmed: optimization_engine/runner.py is sole active runner.

This establishes clear separation between:
- Production code (stable, no runtime LLM dependencies)
- Experimental code (archived for post-MVP exploration)

Part of Phase 1: Core Stabilization & Organization for MVP

Generated with Claude Code

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-11-24 09:12:36 -05:00
Anto01
dd7f0c0f82 Phase 3.3: Multi-objective optimization fix, updated docs & Claude skill 
- Fixed drone gimbal optimization to use proper semantic directions
- Changed from ['minimize', 'minimize'] to ['minimize', 'maximize']
- Updated Claude skill (v2.0) with Phase 3.3 integration
- Added centralized extractor library documentation
- Added multi-objective optimization (Protocol 11) section
- Added NX multi-solution protocol documentation
- Added dashboard integration documentation
- Fixed Pareto front degenerate issue with proper NSGA-II configuration

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

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-11-24 07:49:48 -05:00
Anto01
15c06f7b6c fix: Stop passing design_vars to simulation_runner to match working 50-trial workflow 
**CRITICAL FIX**: FEM results were identical across trials

**Root Cause**:
The LLM runner was passing design_vars to simulation_runner(), which then passed
them to NX Solver's expression_updates parameter. The solve journal tried to
update hardcoded expression names (tip_thickness, support_angle) that don't exist
in the beam model, causing the solver to ignore updates and use cached geometry.

**Solution**:
Match the working 50-trial optimization workflow:
1. model_updater() updates PRT file via NX import journal
2. Part file is closed/flushed to disk
3. simulation_runner() runs WITHOUT passing design_vars
4. NX solver loads SIM file, which references the updated PRT from disk
5. FEM regenerates with updated geometry automatically

**Changes**:
- llm_optimization_runner.py: Call simulation_runner() without arguments
- run_optimization.py: Remove design_vars parameter from simulation_runner closure
- import_expressions.py: Added theSession.Parts.CloseAll() to flush changes
- test_phase_3_2_e2e.py: Fixed remaining variable name bugs

**Test Results**:
✅ Trial 0: objective 7,315,679
✅ Trial 1: objective 9,158.67
✅ Trial 2: objective 7,655.28

FEM results are now DIFFERENT for each trial - optimization working correctly!

**Remaining Issue**: LLM parsing "20 to 30 mm" as 0-1 range (separate fix needed)
 2025-11-17 21:29:21 -05:00
Anto01
b4c0831230 fix: Remove redundant save() call that overwrote NX expression updates 
Critical bug fix for LLM mode optimization:

**Problem**:
- NXParameterUpdater.update_expressions() uses NX journal to import expressions (default use_nx_import=True)
- The NX journal directly updates the PRT file on disk and saves it
- But then run_optimization.py was calling updater.save() afterwards
- save() writes self.content (loaded at initialization) back to file
- This overwrote the NX journal changes with stale binary content!

**Result**: All optimization trials produced identical FEM results because the model was never actually updated.

**Fixes**:
1. Removed updater.save() call from model_updater closure in run_optimization.py
2. Added theSession.Parts.CloseAll() in import_expressions.py to ensure changes are flushed and file is released
3. Fixed test_phase_3_2_e2e.py variable name (best_trial_file → results_file)

**Testing**: Verified expressions persist to disk correctly with standalone test.

Next step: Address remaining issue where FEM results are still identical (likely solve journal not reloading updated PRT).
 2025-11-17 21:24:02 -05:00
Anto01
fe2ef9be6d feat: Implement Study Organization System (Organization v2.0) 
Reorganized simple_beam_optimization study and created templates for future
studies following best practices for clarity, chronology, and self-documentation.

## Study Reorganization (simple_beam_optimization)

**New Directory Structure**:
```
studies/simple_beam_optimization/
├── 1_setup/                    # Pre-optimization setup
│   ├── model/                  # Reference CAD/FEM model
│   └── benchmarking/           # Baseline validation results
├── 2_substudies/               # Optimization runs (numbered chronologically)
│   ├── 01_initial_exploration/
│   ├── 02_validation_3d_3trials/
│   ├── 03_validation_4d_3trials/
│   └── 04_full_optimization_50trials/
└── 3_reports/                  # Study-level analysis
    └── COMPREHENSIVE_BENCHMARK_RESULTS.md
```

**Key Changes**:
1. **Numbered Substudies**: 01_, 02_, 03_, 04_ indicate chronological order
2. **Reorganized Setup**: model/ and benchmarking/ moved to 1_setup/
3. **Centralized Reports**: Study-level docs moved to 3_reports/
4. **Substudy Documentation**: Each substudy has README.md explaining purpose/results

## Updated Metadata

**study_metadata.json** (v2.0):
- Tracks all 4 substudies with creation date, status, purpose
- Includes result summaries (best objective, feasible count)
- Documents new organization version

**Substudies Documented**:
- 01_initial_exploration - Initial design space exploration
- 02_validation_3d_3trials - Validate 3D parameter updates
- 03_validation_4d_3trials - Validate 4D updates including hole_count
- 04_full_optimization_50trials - Full 50-trial optimization

## Templates for Future Studies

**templates/study_template/** - Complete study structure:
- README.md template with study overview format
- study_metadata.json template with v2.0 schema
- Pre-created 1_setup/, 2_substudies/, 3_reports/ directories

**templates/substudy_README_template.md** - Standardized substudy documentation:
- Purpose and hypothesis
- Configuration changes from previous run
- Expected vs actual results
- Validation checklist
- Lessons learned
- Next steps

**templates/HOW_TO_CREATE_A_STUDY.md** - Complete guide:
- Quick start (9 steps from template to first run)
- Substudy workflow
- Directory structure reference
- Naming conventions
- Best practices
- Troubleshooting guide
- Examples

## Benefits

**Clarity**:
- Numbered substudies show chronological progression (01 → 02 → 03 → 04)
- Clear separation: setup vs. optimization runs vs. analysis
- Self-documenting via substudy READMEs

**Discoverability**:
- study_metadata.json provides complete substudy registry
- Each substudy README explains what was tested and why
- Easy to find results for specific runs

**Scalability**:
- Works for small studies (3 substudies) or large studies (50+)
- Chronological numbering scales to 99 substudies
- Template system makes new studies quick to set up

**Reproducibility**:
- Each substudy documents configuration changes
- Purpose and results clearly stated
- Lessons learned captured for future reference

## Implementation Details

**reorganize_study.py** - Migration script:
- Handles locked files gracefully
- Moves files to new structure
- Provides clear progress reporting
- Safe to run multiple times

**Organization Version**: 2.0
- Tracked in study_metadata.json
- Future studies will use this structure by default
- Existing studies can migrate or keep current structure

## Files Added

- templates/study_template/ - Complete study template
- templates/substudy_README_template.md - Substudy documentation template
- templates/HOW_TO_CREATE_A_STUDY.md - Comprehensive creation guide
- reorganize_study.py - Migration script for existing studies

## Files Reorganized (simple_beam_optimization)

**Moved to 1_setup/**:
- model/ → 1_setup/model/ (CAD/FEM reference files)
- substudies/benchmarking/ → 1_setup/benchmarking/
- baseline_validation.json → 1_setup/

**Renamed and Moved to 2_substudies/**:
- substudies/initial_exploration/ → 2_substudies/01_initial_exploration/
- substudies/validation_3trials/ → 2_substudies/02_validation_3d_3trials/
- substudies/validation_4d_3trials/ → 2_substudies/03_validation_4d_3trials/
- substudies/full_optimization_50trials/ → 2_substudies/04_full_optimization_50trials/

**Moved to 3_reports/**:
- COMPREHENSIVE_BENCHMARK_RESULTS.md → 3_reports/

**Substudy-Specific Docs** (moved to substudy directories):
- OPTIMIZATION_RESULTS_50TRIALS.md → 2_substudies/04_full_optimization_50trials/OPTIMIZATION_RESULTS.md

## Documentation Created

Each substudy now has README.md documenting:
- **01_initial_exploration**: Initial exploration purpose
- **02_validation_3d_3trials**: 3D parameter update validation
- **03_validation_4d_3trials**: hole_count validation success
- **04_full_optimization_50trials**: Full results, no feasible designs found

## Next Steps

**For Future Studies**:
1. Copy templates/study_template/
2. Follow templates/HOW_TO_CREATE_A_STUDY.md
3. Use numbered substudies (01_, 02_, ...)
4. Document each substudy with README.md

**For Existing Studies**:
- Can migrate using reorganize_study.py
- Or apply organization v2.0 to new substudies only
- See docs/STUDY_ORGANIZATION.md for migration guide

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

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-11-17 19:20:45 -05:00
Anto01
91e2d7a120 feat: Complete Phase 3.3 - Visualization & Model Cleanup System 
Implemented automated post-processing capabilities for optimization workflows,
including publication-quality visualization and intelligent model cleanup to
manage disk space.

## New Features

### 1. Automated Visualization System (optimization_engine/visualizer.py)

**Capabilities**:
- 6 plot types: convergence, design space, parallel coordinates, sensitivity,
  constraints, objectives
- Publication-quality output: PNG (300 DPI) + PDF (vector graphics)
- Auto-generated plot summary statistics
- Configurable output formats

**Plot Types**:
- Convergence: Objective vs trial number with running best
- Design Space: Parameter evolution colored by performance
- Parallel Coordinates: High-dimensional visualization
- Sensitivity Heatmap: Parameter correlation analysis
- Constraint Violations: Track constraint satisfaction
- Objective Breakdown: Multi-objective contributions

**Usage**:
```bash
# Standalone
python optimization_engine/visualizer.py substudy_dir png pdf

# Automatic (via config)
"post_processing": {"generate_plots": true, "plot_formats": ["png", "pdf"]}
```

### 2. Model Cleanup System (optimization_engine/model_cleanup.py)

**Purpose**: Reduce disk usage by deleting large CAD/FEM files from non-optimal trials

**Strategy**:
- Keep top-N best trials (configurable, default: 10)
- Delete large files: .prt, .sim, .fem, .op2, .f06, .dat, .bdf
- Preserve ALL results.json files (small, critical data)
- Dry-run mode for safety

**Usage**:
```bash
# Standalone
python optimization_engine/model_cleanup.py substudy_dir --keep-top-n 10

# Dry run (preview)
python optimization_engine/model_cleanup.py substudy_dir --dry-run

# Automatic (via config)
"post_processing": {"cleanup_models": true, "keep_top_n_models": 10}
```

**Typical Savings**: 50-90% disk space reduction

### 3. History Reconstruction Tool (optimization_engine/generate_history_from_trials.py)

**Purpose**: Generate history.json from older substudy formats

**Usage**:
```bash
python optimization_engine/generate_history_from_trials.py substudy_dir
```

## Configuration Integration

### JSON Configuration Format (NEW: post_processing section)

```json
{
  "optimization_settings": { ... },
  "post_processing": {
    "generate_plots": true,
    "plot_formats": ["png", "pdf"],
    "cleanup_models": true,
    "keep_top_n_models": 10,
    "cleanup_dry_run": false
  }
}
```

### Runner Integration (optimization_engine/runner.py:656-716)

Post-processing runs automatically after optimization completes:
- Generates plots using OptimizationVisualizer
- Runs model cleanup using ModelCleanup
- Handles exceptions gracefully with warnings
- Prints post-processing summary

## Documentation

### docs/PHASE_3_3_VISUALIZATION_AND_CLEANUP.md
Complete feature documentation:
- Feature overview and capabilities
- Configuration guide
- Plot type descriptions with use cases
- Benefits and examples
- Troubleshooting section
- Future enhancements

### docs/OPTUNA_DASHBOARD.md
Optuna dashboard integration guide:
- Quick start instructions
- Real-time monitoring during optimization
- Comparison: Optuna dashboard vs Atomizer matplotlib
- Recommendation: Use both (Optuna for monitoring, Atomizer for reports)

### docs/STUDY_ORGANIZATION.md (NEW)
Study directory organization guide:
- Current organization analysis
- Recommended structure with numbered substudies
- Migration guide (reorganize existing or apply to future)
- Best practices for study/substudy/trial levels
- Naming conventions
- Metadata format recommendations

## Testing & Validation

**Tested on**: simple_beam_optimization/full_optimization_50trials (50 trials)

**Results**:
- Generated 6 plots × 2 formats = 12 files successfully
- Plots saved to: studies/.../substudies/full_optimization_50trials/plots/
- All plot types working correctly
- Unicode display issue fixed (replaced ✓ with "SUCCESS:")

**Example Output**:
```
POST-PROCESSING
===========================================================

Generating visualization plots...
  - Generating convergence plot...
  - Generating design space exploration...
  - Generating parallel coordinate plot...
  - Generating sensitivity heatmap...
  Plots generated: 2 format(s)
  Improvement: 23.1%
  Location: studies/.../plots

Cleaning up trial models...
  Deleted 320 files from 40 trials
  Space freed: 1542.3 MB
  Kept top 10 trial models
===========================================================
```

## Benefits

**Visualization**:
- Publication-ready plots without manual post-processing
- Automated generation after each optimization
- Comprehensive coverage (6 plot types)
- Embeddable in reports, papers, presentations

**Model Cleanup**:
- 50-90% disk space savings typical
- Selective retention (keeps best trials)
- Safe (preserves all critical data)
- Traceable (cleanup log documents deletions)

**Organization**:
- Clear study directory structure recommendations
- Chronological substudy numbering
- Self-documenting substudy system
- Scalable for small and large projects

## Files Modified

- optimization_engine/runner.py - Added _run_post_processing() method
- studies/simple_beam_optimization/beam_optimization_config.json - Added post_processing section
- studies/simple_beam_optimization/substudies/full_optimization_50trials/plots/ - Generated plots

## Files Added

- optimization_engine/visualizer.py - Visualization system
- optimization_engine/model_cleanup.py - Model cleanup system
- optimization_engine/generate_history_from_trials.py - History reconstruction
- docs/PHASE_3_3_VISUALIZATION_AND_CLEANUP.md - Complete documentation
- docs/OPTUNA_DASHBOARD.md - Optuna dashboard guide
- docs/STUDY_ORGANIZATION.md - Study organization guide

## Dependencies

**Required** (for visualization):
- matplotlib >= 3.10
- numpy < 2.0 (pyNastran compatibility)
- pandas >= 2.3

**Optional** (for real-time monitoring):
- optuna-dashboard

## Known Issues & Workarounds

**Issue**: atomizer environment has corrupted matplotlib/numpy dependencies
**Workaround**: Use test_env environment (has working dependencies)
**Long-term Fix**: Rebuild atomizer environment cleanly (pending)

**Issue**: Older substudies missing history.json
**Solution**: Use generate_history_from_trials.py to reconstruct

## Next Steps

**Immediate**:
1. Rebuild atomizer environment with clean dependencies
2. Test automated post-processing on new optimization run
3. Consider applying study organization recommendations to existing study

**Future Enhancements** (Phase 3.4):
- Interactive HTML plots (Plotly)
- Automated report generation (Markdown → PDF)
- Video animation of design evolution
- 3D scatter plots for high-dimensional spaces
- Statistical analysis (confidence intervals, significance tests)
- Multi-substudy comparison reports

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

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-11-17 19:07:41 -05:00
Anto01
3a0ffb572c feat: Add centralized configuration system and Phase 3.2 enhancements 
Major Features Added:

1. Centralized Configuration System (config.py)
   - Single source of truth for all NX and environment paths
   - Change NX version in ONE place: NX_VERSION = "2412"
   - Change Python environment in ONE place: PYTHON_ENV_NAME = "atomizer"
   - Automatic path derivation and validation
   - Helper functions: get_nx_journal_command()
   - Future-proof: Easy to upgrade when NX 2506+ released

2. NX Path Corrections (Critical Fix)
   - Fixed all incorrect Simcenter3D_2412 references to NX2412
   - Updated nx_updater.py to use config.NX_RUN_JOURNAL
   - Updated dashboard/api/app.py to use config.NX_RUN_JOURNAL
   - Corrected material library path to NX2412/UGII/materials
   - All files now use correct NX2412 installation

3. NX Expression Import System
   - Dual-method expression gathering (.exp export + binary parsing)
   - Robust handling of all NX expression types
   - Support for formulas, units, and dependencies
   - Documented in docs/NX_EXPRESSION_IMPORT_SYSTEM.md

4. Study Management & Analysis Tools
   - StudyCreator: Unified interface for study/substudy creation
   - BenchmarkingSubstudy: Automated baseline analysis
   - ComprehensiveResultsAnalyzer: Multi-result extraction from .op2
   - Expression extractor generator (LLM-powered)

5. 50-Trial Beam Optimization Complete
   - Full optimization results documented
   - Best design: 23.1% improvement over baseline
   - Comprehensive analysis with plots and insights
   - Results in studies/simple_beam_optimization/

Documentation Updates:
- docs/SYSTEM_CONFIGURATION.md - System paths and validation
- docs/QUICK_CONFIG_REFERENCE.md - Quick config change guide
- docs/NX_EXPRESSION_IMPORT_SYSTEM.md - Expression import details
- docs/OPTIMIZATION_WORKFLOW.md - Complete workflow guide
- Updated README.md with NX2412 paths

Files Modified:
- config.py (NEW) - Central configuration system
- optimization_engine/nx_updater.py - Now uses config
- dashboard/api/app.py - Now uses config
- optimization_engine/study_creator.py - Enhanced features
- optimization_engine/benchmarking_substudy.py - New analyzer
- optimization_engine/comprehensive_results_analyzer.py - Multi-result extraction
- optimization_engine/result_extractors/generated/extract_expression.py - Generated extractor

Cleanup:
- Removed all temporary test files
- Removed migration scripts (no longer needed)
- Clean production-ready codebase

Strategic Impact:
- Configuration maintenance time: reduced from hours to seconds
- Path consistency: 100% enforced across codebase
- Future NX upgrades: Edit ONE variable in config.py
- Foundation for Phase 3.2 Integration completion

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

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-11-17 14:36:00 -05:00
Anto01
8b14f6e800 feat: Add robust NX expression import system for all expression types 
Major Enhancement:
- Implemented .exp file-based expression updates via NX journal scripts
- Fixes critical issue with feature-linked expressions (e.g., hole_count)
- Supports ALL NX expression types including binary-stored ones
- Full 4D design space validation completed successfully

New Components:
1. import_expressions.py - NX journal for .exp file import
   - Uses NXOpen.ExpressionCollection.ImportFromFile()
   - Replace mode overwrites existing values
   - Automatic model update and save
   - Comprehensive error handling

2. export_expressions.py - NX journal for .exp file export
   - Exports all expressions to text format
   - Used for unit detection and verification

3. Enhanced nx_updater.py
   - New update_expressions_via_import() method
   - Automatic unit detection from .exp export
   - Creates study-variable-only .exp files
   - Replaces fragile binary .prt editing

Technical Details:
- .exp Format: [Units]name=value (e.g., [MilliMeter]beam_length=5000)
- Unitless expressions: name=value (e.g., hole_count=10)
- Robustness: Native NX functionality, no regex failures
- Performance: < 1 second per update operation

Validation:
- Simple Beam Optimization study (4D design space)
  * beam_half_core_thickness: 10-40 mm
  * beam_face_thickness: 10-40 mm
  * holes_diameter: 150-450 mm
  * hole_count: 5-15 (integer)

Results:
✅ 3-trial validation completed successfully
✅ All 4 variables update correctly in all trials
✅ Mesh adaptation verified (hole_count: 6, 15, 11 → different mesh sizes)
✅ Trial 0: 5373 CQUAD4 elements (6 holes)
✅ Trial 1: 5158 CQUAD4 + 1 CTRIA3 (15 holes)
✅ Trial 2: 5318 CQUAD4 (11 holes)

Problem Solved:
- hole_count expression was not updating with binary .prt editing
- Expression stored in feature parameter, not accessible via text regex
- Binary format prevented reliable text-based updates

Solution:
- Use NX native expression import/export
- Works for ALL expressions (text and binary-stored)
- Automatic unit handling
- Model update integrated in journal

Documentation:
- New: docs/NX_EXPRESSION_IMPORT_SYSTEM.md (comprehensive guide)
- Updated: CHANGELOG.md with Phase 3.2 progress
- Study: studies/simple_beam_optimization/ (complete example)

Files Added:
- optimization_engine/import_expressions.py
- optimization_engine/export_expressions.py
- docs/NX_EXPRESSION_IMPORT_SYSTEM.md
- studies/simple_beam_optimization/ (full study)

Files Modified:
- optimization_engine/nx_updater.py
- CHANGELOG.md

Compatibility:
- NX 2412 tested and verified
- Python 3.10+
- Works with all NX expression types

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

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-11-17 12:34:06 -05:00
Anto01
2f3afc3813 feat: Add substudy system with live history tracking and workflow fixes 
Major Features:
- Hierarchical substudy system (like NX Solutions/Subcases)
  * Shared model files across all substudies
  * Independent configuration per substudy
  * Continuation support from previous substudies
  * Real-time incremental history updates
- Live history tracking with optimization_history_incremental.json
- Complete bracket_displacement_maximizing study with substudy examples

Core Fixes:
- Fixed expression update workflow to pass design_vars through simulation_runner
  * Restored working NX journal expression update mechanism
  * OP2 timestamp verification instead of file deletion
  * Resolved issue where all trials returned identical objective values
- Fixed LLMOptimizationRunner to pass design variables to simulation runner
- Enhanced NXSolver with timestamp-based file regeneration verification

New Components:
- optimization_engine/llm_optimization_runner.py - LLM-driven optimization runner
- optimization_engine/optimization_setup_wizard.py - Phase 3.3 setup wizard
- studies/bracket_displacement_maximizing/ - Complete substudy example
  * run_substudy.py - Substudy runner with continuation
  * run_optimization.py - Standalone optimization runner
  * config/substudy_template.json - Template for new substudies
  * substudies/coarse_exploration/ - 20-trial coarse search
  * substudies/fine_tuning/ - 50-trial refinement (continuation example)
  * SUBSTUDIES_README.md - Complete substudy documentation

Technical Improvements:
- Incremental history saving after each trial (optimization_history_incremental.json)
- Expression update workflow: .prt update → NX journal receives values → geometry update → FEM update → solve
- Trial indexing fix in substudy result saving
- Updated README with substudy system documentation

Testing:
- Successfully ran 20-trial coarse_exploration substudy
- Verified different objective values across trials (workflow fix validated)
- Confirmed live history updates in real-time
- Tested shared model file usage across substudies

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

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-11-16 21:29:54 -05:00
Anto01
0a7cca9c6a feat: Complete Phase 2.5-2.7 - Intelligent LLM-Powered Workflow Analysis 
This commit implements three major architectural improvements to transform
Atomizer from static pattern matching to intelligent AI-powered analysis.

## Phase 2.5: Intelligent Codebase-Aware Gap Detection ✅

Created intelligent system that understands existing capabilities before
requesting examples:

**New Files:**
- optimization_engine/codebase_analyzer.py (379 lines)
  Scans Atomizer codebase for existing FEA/CAE capabilities

- optimization_engine/workflow_decomposer.py (507 lines, v0.2.0)
  Breaks user requests into atomic workflow steps
  Complete rewrite with multi-objective, constraints, subcase targeting

- optimization_engine/capability_matcher.py (312 lines)
  Matches workflow steps to existing code implementations

- optimization_engine/targeted_research_planner.py (259 lines)
  Creates focused research plans for only missing capabilities

**Results:**
- 80-90% coverage on complex optimization requests
- 87-93% confidence in capability matching
- Fixed expression reading misclassification (geometry vs result_extraction)

## Phase 2.6: Intelligent Step Classification ✅

Distinguishes engineering features from simple math operations:

**New Files:**
- optimization_engine/step_classifier.py (335 lines)

**Classification Types:**
1. Engineering Features - Complex FEA/CAE needing research
2. Inline Calculations - Simple math to auto-generate
3. Post-Processing Hooks - Middleware between FEA steps

## Phase 2.7: LLM-Powered Workflow Intelligence ✅

Replaces static regex patterns with Claude AI analysis:

**New Files:**
- optimization_engine/llm_workflow_analyzer.py (395 lines)
  Uses Claude API for intelligent request analysis
  Supports both Claude Code (dev) and API (production) modes

- .claude/skills/analyze-workflow.md
  Skill template for LLM workflow analysis integration

**Key Breakthrough:**
- Detects ALL intermediate steps (avg, min, normalization, etc.)
- Understands engineering context (CBUSH vs CBAR, directions, metrics)
- Distinguishes OP2 extraction from part expression reading
- Expected 95%+ accuracy with full nuance detection

## Test Coverage

**New Test Files:**
- tests/test_phase_2_5_intelligent_gap_detection.py (335 lines)
- tests/test_complex_multiobj_request.py (130 lines)
- tests/test_cbush_optimization.py (130 lines)
- tests/test_cbar_genetic_algorithm.py (150 lines)
- tests/test_step_classifier.py (140 lines)
- tests/test_llm_complex_request.py (387 lines)

All tests include:
- UTF-8 encoding for Windows console
- atomizer environment (not test_env)
- Comprehensive validation checks

## Documentation

**New Documentation:**
- docs/PHASE_2_5_INTELLIGENT_GAP_DETECTION.md (254 lines)
- docs/PHASE_2_7_LLM_INTEGRATION.md (227 lines)
- docs/SESSION_SUMMARY_PHASE_2_5_TO_2_7.md (252 lines)

**Updated:**
- README.md - Added Phase 2.5-2.7 completion status
- DEVELOPMENT_ROADMAP.md - Updated phase progress

## Critical Fixes

1. **Expression Reading Misclassification** (lines cited in session summary)
   - Updated codebase_analyzer.py pattern detection
   - Fixed workflow_decomposer.py domain classification
   - Added capability_matcher.py read_expression mapping

2. **Environment Standardization**
   - All code now uses 'atomizer' conda environment
   - Removed test_env references throughout

3. **Multi-Objective Support**
   - WorkflowDecomposer v0.2.0 handles multiple objectives
   - Constraint extraction and validation
   - Subcase and direction targeting

## Architecture Evolution

**Before (Static & Dumb):**
User Request → Regex Patterns → Hardcoded Rules → Missed Steps ❌

**After (LLM-Powered & Intelligent):**
User Request → Claude AI Analysis → Structured JSON →
├─ Engineering (research needed)
├─ Inline (auto-generate Python)
├─ Hooks (middleware scripts)
└─ Optimization (config) ✅

## LLM Integration Strategy

**Development Mode (Current):**
- Use Claude Code directly for interactive analysis
- No API consumption or costs
- Perfect for iterative development

**Production Mode (Future):**
- Optional Anthropic API integration
- Falls back to heuristics if no API key
- For standalone batch processing

## Next Steps

- Phase 2.8: Inline Code Generation
- Phase 2.9: Post-Processing Hook Generation
- Phase 3: MCP Integration for automated documentation research

🚀 Generated with Claude Code

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-11-16 13:35:41 -05:00