- Add TrialManager (trial_manager.py) for consistent trial_NNNN naming
- Add DashboardDB (dashboard_db.py) for Optuna-compatible database schema
- Update CLAUDE.md with trial management documentation
- Update ATOMIZER_CONTEXT.md with v1.8 trial system
- Update cheatsheet v2.2 with new utilities
- Update SYS_14 protocol to v2.3 with TrialManager integration
- Add LAC learnings for trial management patterns
- Add archive/README.md for deprecated code policy
Key principles:
- Trial numbers NEVER reset (monotonic)
- Folders NEVER get overwritten
- Database always synced with filesystem
- Surrogate predictions are NOT trials (only FEA results)
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Adds tests/debug_lateral_discrepancy.py to investigate differences between
Zernike OPD lateral displacement reporting and Simcenter post-processing.
Key findings documented:
- OPD reports sqrt(dx² + dy²) - combined XY magnitude
- Simcenter shows individual components (dx or dy)
- Both are correct, OPD magnitude is more meaningful for optics
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- Replace Standard (Z-only) extraction with OPD method (X,Y,Z displacement)
- Add toggle buttons to switch between WFE, ΔX, ΔY, ΔZ views
- Show method comparison metrics (OPD vs Standard RMS difference)
- Display lateral displacement statistics (max/RMS)
- Fall back to Standard method if BDF geometry file not found
- Use ZernikeOPDExtractor for more accurate WFE computation (+8-11%)
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Major improvements to Zernike WFE visualization:
- Add ZernikeDashboardInsight: Unified dashboard with all orientations (40°, 60°, 90°)
on one page with light theme and executive summary
- Add OPD method toggle: Switch between Standard (Z-only) and OPD (X,Y,Z) methods
in ZernikeWFEInsight with interactive buttons
- Add lateral displacement maps: Visualize X,Y displacement for each orientation
- Add displacement component views: Toggle between WFE, ΔX, ΔY, ΔZ in relative views
- Add metrics comparison table showing both methods side-by-side
New extractors:
- extract_zernike_figure.py: ZernikeOPDExtractor using BDF geometry interpolation
- extract_zernike_opd.py: Parabola-based OPD with focal length
Key finding: OPD method gives 8-11% higher WFE values than Standard method
(more conservative/accurate for surfaces with lateral displacement under gravity)
Documentation updates:
- SYS_12: Added E22 ZernikeOPD as recommended method
- SYS_16: Added ZernikeDashboard, updated ZernikeWFE with OPD features
- Cheatsheet: Added Zernike method comparison table
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Dashboard integration for Study Insights module (SYS_16):
- Backend: New /api/insights/ routes for generating and viewing insights
- Frontend: New Insights.tsx page with Plotly visualization
- Navigation: Added Insights tab between Analysis and Results
Available insight types:
- Zernike WFE (wavefront error for mirrors)
- Stress Field (Von Mises stress contours)
- Modal Analysis (natural frequencies/mode shapes)
- Thermal Field (temperature distribution)
- Design Space (parameter-objective exploration)
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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.
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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
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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)
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Comprehensive guide teaching users how to interact with Atomizer so that
the learning system evolves correctly. Covers:
- The right mindset (colleague, not tool)
- Starting sessions with proper context
- Communicating goals, constraints, preferences
- Creating and running optimization studies
- Analyzing and validating results
- Reporting errors effectively
- Contributing to LAC (recording insights, outcomes, workarounds)
- Ending sessions properly to capture learnings
Includes:
- Mermaid diagrams for learning loop and flows
- Good vs bad examples for every interaction type
- Complete example session transcript
- Quick reference card for common patterns
- Golden rules summary
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Add persistent knowledge system that enables Atomizer to learn from every
session and improve over time.
## New Files
- knowledge_base/lac.py: LAC class with optimization memory, session insights,
and skill evolution tracking
- knowledge_base/__init__.py: Package initialization
- .claude/skills/modules/learning-atomizer-core.md: Full LAC skill documentation
- docs/07_DEVELOPMENT/ATOMIZER_CLAUDE_CODE_INSTRUCTIONS.md: Master instructions
## Updated Files
- CLAUDE.md: Added LAC section, communication style, AVERVS execution framework,
error classification, and "Atomizer Claude" identity
- 00_BOOTSTRAP.md: Added session startup/closing checklists with LAC integration
- 01_CHEATSHEET.md: Added LAC CLI and Python API quick reference
- 02_CONTEXT_LOADER.md: Added LAC query section and anti-pattern
## LAC Features
- Query similar past optimizations before starting new ones
- Record insights (failures, success patterns, workarounds)
- Record optimization outcomes for future reference
- Suggest protocol improvements based on discoveries
- Simple JSONL storage (no database required)
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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.
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The Method Selector now uses relative accuracy thresholds to assess
NN suitability by comparing NN error to problem variability (CV ratio).
NNQualityAssessor features:
- Physics-based objective classification (linear, smooth, nonlinear, chaotic)
- CV ratio computation: nn_error / coefficient_of_variation
- Turbo suitability score based on relative thresholds
- Data collection from validation_report.json, turbo_report.json, and study.db
Quality thresholds by objective type:
- Linear (mass, volume): max 2% error, CV ratio < 0.5
- Smooth (frequency): max 5% error, CV ratio < 1.0
- Nonlinear (stress, stiffness): max 10% error, CV ratio < 2.0
- Chaotic (contact, buckling): max 20% error, CV ratio < 3.0
CLI output now includes:
- Per-objective NN quality table with error, CV, ratio, and quality indicator
- Turbo suitability and hybrid suitability percentages
- Warnings when NN error exceeds physics-based thresholds
Updated SYS_15_METHOD_SELECTOR.md to v2.0 with full NN Quality Assessment documentation.
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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.
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Dashboard enhancements:
- Add Analysis page with tabs: Overview, Parameters, Pareto, Correlations, Constraints, Surrogate, Runs
- Add PlotlyCorrelationHeatmap for parameter-objective correlation analysis
- Add PlotlyFeasibilityChart for constraint satisfaction visualization
- Add PlotlySurrogateQuality for FEA vs NN prediction comparison
- Add PlotlyRunComparison for comparing optimization runs within a study
Real-time improvements:
- Replace watchdog file-watching with SQLite database polling for better Windows reliability
- Add DatabasePoller class with 2-second polling interval
- Enhanced WebSocket messages: trial_completed, new_best, pareto_update, progress
Desktop notifications:
- Add useNotifications hook using Web Notifications API
- Add NotificationSettings toggle component
- Notify users when new best solutions are found
Config editor:
- Add PUT /studies/{study_id}/config endpoint with auto-backup
- Add ConfigEditor modal with tabs: General, Variables, Objectives, Settings, JSON
- Prevents editing while optimization is running
Enhanced Pareto visualization:
- Add dark mode styling with transparent backgrounds
- Add stats bar showing Pareto, FEA, NN, and infeasible counts
- Add Pareto front connecting line for 2D view
- Add table showing top 10 Pareto-optimal solutions
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Phase 1 - Accurate study status detection:
- Add is_optimization_running() to check for active processes
- Add get_accurate_study_status() with proper status logic
- Status now: not_started, running, paused, completed
- Add "paused" status styling (orange) to Home page
Phase 2 - Global Claude terminal:
- Create ClaudeTerminalContext for app-level state
- Create GlobalClaudeTerminal floating component
- Terminal persists across page navigation
- Shows green indicator when connected
- Remove inline terminal from Dashboard
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Co-Authored-By: Claude <noreply@anthropic.com>
- Reorganize dashboard: control panel on top, charts stacked vertically
- Add Set Context button to Claude terminal for study awareness
- Add conda environment instructions to CLAUDE.md
- Fix STUDY_REPORT.md location in generate-report.md skill
- Claude terminal now sends study context with skills reminder
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- 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
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- Add embedded Claude Code terminal with xterm.js for full CLI experience
- Create WebSocket PTY backend for real-time terminal communication
- Add terminal status endpoint to check CLI availability
- Update dashboard to use Claude Code terminal instead of API chat
- Add optimization control panel with start/stop/validate actions
- Add study context provider for global state management
- Update frontend with new dependencies (xterm.js addons)
- Comprehensive README documentation for all new features
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- python launch_dashboard.py starts both backend and frontend
- Ctrl+C gracefully shuts down both servers
- Color-coded terminal output for status
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- Add ConvergencePlot component with running best, statistics, gradient fill
- Add ParameterImportanceChart with Pearson correlation analysis
- Add StudyReportViewer with KaTeX math rendering and full markdown support
- Update pruning endpoint to query Optuna database directly
- Add /report endpoint for STUDY_REPORT.md files
- Fix chart data transformation for single/multi-objective studies
- Update Protocol 13 documentation with new components
- Update generate-report skill with dashboard integration
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- environment.yml: Added PyTorch with CUDA 12.1, PyG (torch-geometric),
and TensorBoard for neural network training
- INSTALL_INSTRUCTIONS.md: Step-by-step guide for installing Miniconda
and setting up the Atomizer environment
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- requirements.txt: Added all dependencies including PyTorch,
torch-geometric, tensorboard for neural network training
- install.bat: One-click installation script that installs all
dependencies with proper version constraints
- train_neural.bat: Training script that runs parametric neural
network training on collected FEA data
Usage:
1. Double-click install.bat to install dependencies
2. Double-click train_neural.bat to train on bracket study
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Rebuilds missing neural network components based on documentation:
- neural_models/parametric_predictor.py: Design-conditioned GNN that
predicts all 4 optimization objectives (mass, frequency, displacement,
stress) directly from design parameters. ~500K trainable parameters.
- train_parametric.py: Training script with multi-objective loss,
checkpoint saving with normalization stats, and TensorBoard logging.
- Updated __init__.py to export ParametricFieldPredictor and
create_parametric_model for use by optimization_engine/neural_surrogate.py
These files enable the neural acceleration workflow:
1. Collect FEA training data (189 trials already collected)
2. Train parametric model: python train_parametric.py --train_dir ...
3. Run neural-accelerated optimization with --enable-nn flag
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Permanently integrates the Atomizer-Field GNN surrogate system:
- neural_models/: Graph Neural Network for FEA field prediction
- batch_parser.py: Parse training data from FEA exports
- train.py: Neural network training pipeline
- predict.py: Inference engine for fast predictions
This enables 600x-2200x speedup over traditional FEA by replacing
expensive simulations with millisecond neural network predictions.
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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
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Problem:
When running optimization studies with multiple solutions (e.g., static + modal),
NX opens solution monitor windows for each trial. These windows superpose and cause
usability issues during long optimization runs.
Solution:
- Automatically disable solution monitor when solving all solutions (solution_name=None)
- Loop through all solutions and set "solution monitor" property to False
- Implemented in solve_simulation.py before solve execution (lines 271-295)
- Includes error handling with graceful fallback
Benefits:
- No monitor window pile-up during optimization studies
- Better performance (no GUI overhead)
- No user configuration required - works automatically
- Based on user-recorded journal (journal_monitor_window_off.py)
Documentation:
- Updated docs/NX_MULTI_SOLUTION_PROTOCOL.md with solution monitor control section
- Added implementation details and when the feature activates
- Cross-referenced user's recorded journal
Implementation: optimization_engine/solve_simulation.py
Documentation: docs/NX_MULTI_SOLUTION_PROTOCOL.md
Reference: nx_journals/user_generated_journals/journal_monitor_window_off.py
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)
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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
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Implements JSON Schema validation for optimization configurations to ensure
consistency across all studies and prevent configuration errors.
Added:
- optimization_engine/schemas/optimization_config_schema.json
- Comprehensive schema for Protocol 10 & 11 configurations
- Validates objectives, constraints, design variables, simulation settings
- Enforces standard field names (goal, bounds, parameter, threshold)
- optimization_engine/config_manager.py
- ConfigManager class with schema validation
- CLI tool: python config_manager.py <config.json>
- Type-safe accessor methods for config elements
- Custom validations: bounds check, multi-objective consistency, location check
- optimization_engine/schemas/README.md
- Complete documentation of standard configuration format
- Validation examples and common error fixes
- Migration guidance for legacy configs
- docs/07_DEVELOPMENT/Phase_1_2_Implementation_Plan.md
- Detailed implementation plan for remaining Phase 1.2 tasks
- Migration tool design, integration guide, testing plan
Testing:
- Validated drone_gimbal_arm_optimization config successfully
- ConfigManager works with drone_gimbal format (new standard)
- Identifies legacy format issues in bracket studies
Standards Established:
- Configuration location: studies/{name}/1_setup/
- Objective direction: "goal" not "type"
- Design var bounds: "bounds": [min, max] not "min"/"max"
- Design var name: "parameter" not "name"
- Constraint threshold: "threshold" not "value"
Next Steps (Phase 1.2.1+):
- Config migration tool for legacy studies
- Integration with run_optimization.py
- Update create-study Claude skill with schema reference
- Migrate bracket studies to new format
Relates to: Phase 1.2 MVP Development Plan
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