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>
Atomizer Studies Directory
This directory contains optimization studies for the Atomizer framework. Each study is a self-contained workspace for running NX optimization campaigns.
Directory Structure
studies/
├── README.md # This file
├── _templates/ # Study templates for quick setup
│ ├── basic_stress_optimization/
│ ├── multi_objective/
│ └── constrained_optimization/
├── _archive/ # Completed/old studies
│ └── YYYY-MM-DD_study_name/
└── [active_studies]/ # Your active optimization studies
└── bracket_stress_minimization/ # Example study
Study Folder Structure
Each study should follow this standardized structure:
study_name/
├── README.md # Study description, objectives, notes
├── optimization_config.json # Atomizer configuration file
│
├── model/ # FEA model files (NX or other solvers)
│ ├── model.prt # NX part file
│ ├── model.sim # NX Simcenter simulation file
│ ├── model.fem # FEM file
│ └── assembly.asm # NX assembly (if applicable)
│
├── optimization_results/ # Generated by Atomizer (DO NOT COMMIT)
│ ├── optimization.log # High-level optimization progress log
│ ├── trial_logs/ # Detailed iteration logs (one per trial)
│ │ ├── trial_000_YYYYMMDD_HHMMSS.log
│ │ ├── trial_001_YYYYMMDD_HHMMSS.log
│ │ └── ...
│ ├── history.json # Complete optimization history
│ ├── history.csv # CSV format for analysis
│ ├── optimization_summary.json # Best results summary
│ ├── study_*.db # Optuna database files
│ └── study_*_metadata.json # Study metadata for resumption
│
├── analysis/ # Post-optimization analysis
│ ├── plots/ # Generated visualizations
│ ├── reports/ # Generated PDF/HTML reports
│ └── sensitivity_analysis.md # Analysis notes
│
└── notes.md # Engineering notes, decisions, insights
Creating a New Study
Option 1: From Template
# Copy a template
cp -r studies/_templates/basic_stress_optimization studies/my_new_study
cd studies/my_new_study
# Edit the configuration
# - Update optimization_config.json
# - Place your .sim, .prt, .fem files in model/
# - Update README.md with study objectives
Option 2: Manual Setup
# Create study directory
mkdir -p studies/my_study/{model,analysis/plots,analysis/reports}
# Create config file
# (see _templates/ for examples)
# Add your files
# - Place all FEA files (.prt, .sim, .fem) in model/
# - Edit optimization_config.json
Running an Optimization
# Navigate to project root
cd /path/to/Atomizer
# Run optimization for a study
python run_study.py --study studies/my_study
# Or use the full path to config
python -c "from optimization_engine.runner import OptimizationRunner; ..."
Configuration File Format
The optimization_config.json file defines the optimization setup:
{
"design_variables": [
{
"name": "thickness",
"type": "continuous",
"bounds": [3.0, 8.0],
"units": "mm",
"initial_value": 5.0
}
],
"objectives": [
{
"name": "minimize_stress",
"description": "Minimize maximum von Mises stress",
"extractor": "stress_extractor",
"metric": "max_von_mises",
"direction": "minimize",
"weight": 1.0,
"units": "MPa"
}
],
"constraints": [
{
"name": "displacement_limit",
"description": "Maximum allowable displacement",
"extractor": "displacement_extractor",
"metric": "max_displacement",
"type": "upper_bound",
"limit": 1.0,
"units": "mm"
}
],
"optimization_settings": {
"n_trials": 50,
"sampler": "TPE",
"n_startup_trials": 20,
"tpe_n_ei_candidates": 24,
"tpe_multivariate": true
},
"model_info": {
"sim_file": "model/model.sim",
"note": "Brief description"
}
}
Results Organization
All optimization results are stored in optimization_results/ within each study folder.
Optimization Log (optimization.log)
High-level overview of the entire optimization run:
- Optimization configuration (design variables, objectives, constraints)
- One compact line per trial showing design variables and results
- Easy to scan and monitor optimization progress
- Perfect for quick reviews and debugging
Example format:
[08:15:35] Trial 0 START | tip_thickness=20.450, support_angle=32.100
[08:15:42] Trial 0 COMPLETE | max_von_mises=245.320, max_displacement=0.856
[08:15:45] Trial 1 START | tip_thickness=18.230, support_angle=28.900
[08:15:51] Trial 1 COMPLETE | max_von_mises=268.450, max_displacement=0.923
Trial Logs (trial_logs/)
Detailed per-trial logs showing complete iteration trace:
- Design variable values for the trial
- Complete optimization configuration
- Execution timeline (pre_solve, solve, post_solve, extraction)
- Extracted results (stress, displacement, etc.)
- Constraint evaluations
- Hook execution trace
- Solver output and warnings
Example: trial_005_20251116_143022.log
These logs are invaluable for:
- Debugging failed trials
- Understanding what happened in specific iterations
- Verifying solver behavior
- Tracking hook execution
History Files
Structured data for analysis and visualization:
- history.json: Complete trial-by-trial results in JSON format
- history.csv: Same data in CSV for Excel/plotting
- optimization_summary.json: Best parameters and final results
Optuna Database
Study persistence for resuming optimizations:
- study_NAME.db: SQLite database storing all trial data
- study_NAME_metadata.json: Study metadata and configuration hash
The database allows you to:
- Resume interrupted optimizations
- Add more trials to a completed study
- Query optimization history programmatically
Best Practices
Study Naming
- Use descriptive names:
bracket_stress_minimizationnottest1 - Include objective:
wing_mass_displacement_tradeoff - Version if iterating:
bracket_v2_reduced_mesh
Documentation
- Always fill out README.md in each study folder
- Document design decisions in notes.md
- Keep analysis/ folder updated with plots and reports
Version Control
Add to .gitignore:
studies/*/optimization_results/
studies/*/analysis/plots/
studies/*/__pycache__/
Commit to git:
studies/*/README.md
studies/*/optimization_config.json
studies/*/notes.md
studies/*/model/*.sim
studies/*/model/*.prt (optional - large CAD files)
studies/*/model/*.fem
Archiving Completed Studies
When a study is complete:
# Archive the study
mv studies/completed_study studies/_archive/2025-11-16_completed_study
# Update _archive/README.md with study summary
Study Templates
Basic Stress Optimization
- Single objective: minimize stress
- Single design variable
- Simple mesh
- Good for learning/testing
Multi-Objective Optimization
- Multiple competing objectives (stress, mass, displacement)
- Pareto front analysis
- Weighted sum approach
Constrained Optimization
- Objectives with hard constraints
- Demonstrates constraint handling
- Pruned trials when constraints violated
Troubleshooting
Study won't resume
Check that optimization_config.json hasn't changed. The config hash is stored in metadata and verified on resume.
Missing trial logs or optimization.log
Ensure logging plugins are enabled:
optimization_engine/plugins/pre_solve/detailed_logger.py- Creates detailed trial logsoptimization_engine/plugins/pre_solve/optimization_logger.py- Creates high-level optimization.logoptimization_engine/plugins/post_extraction/log_results.py- Appends results to trial logsoptimization_engine/plugins/post_extraction/optimization_logger_results.py- Appends results to optimization.log
Results directory missing
The directory is created automatically on first run. Check file permissions.
Advanced: Custom Hooks
Studies can include custom hooks in a hooks/ folder:
my_study/
├── hooks/
│ ├── pre_solve/
│ │ └── custom_parameterization.py
│ └── post_extraction/
│ └── custom_objective.py
└── ...
These hooks are automatically loaded if present.
Questions?
- See main README.md for Atomizer documentation
- See DEVELOPMENT_ROADMAP.md for planned features
- Check docs/ for detailed guides