Files
Atomizer/templates/substudy_README_template.md
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

3.0 KiB

Substudy NN: [Descriptive Name]

Date: YYYY-MM-DD Status: [planned | running | completed | failed] Trials: N

Purpose

[Why was this substudy created? What hypothesis is being tested?]

Example:

  • Validate parameter update mechanism
  • Explore promising design region identified in previous run
  • Test relaxed constraint settings
  • Sensitivity analysis for specific parameter

Configuration Changes

From Previous Substudy (or baseline):

[List what changed compared to previous substudy or baseline config]

Examples:

  • Increased beam thickness bounds from 40mm to 60mm
  • Changed displacement constraint from 10mm to 12.5mm
  • Adjusted objective weights: displacement 50% (was 33%), stress 25% (was 33%), mass 25% (was 34%)
  • Narrowed hole_count range to 9-13 (was 5-15)
  • Increased trial count from 10 to 50

Design Variables:

  • var1: min-max units [if changed, note it]
  • var2: min-max units

Objectives:

  • objective1: weight [if changed, note it]
  • objective2: weight

Sampler: [TPE | Random | Grid | etc.]

Expected Outcome

[What do you hope to learn or achieve?]

Examples:

  • Find feasible designs meeting relaxed constraint
  • Identify which parameter has most impact on displacement
  • Validate that hole_count updates correctly
  • Achieve 10% improvement over best result from substudy 03

Actual Results

[Fill in after completion]

Status: [ SUCCESS | ⚠️ PARTIAL | FAILED]

Best Trial: #XX

  • Objective: XXX
  • Key metrics: [e.g., displacement: X.X mm, mass: XXXX kg]
  • Feasible: [Yes/No]

Key Findings:

  • Finding 1
  • Finding 2
  • Finding 3

Statistics:

  • Total trials: XX
  • Feasible designs: N / N_total (XX%)
  • Best objective: XXX
  • Improvement over previous: XX%

Validation

[If this was a validation run, document what was validated]

Examples:

  • All design variables updated correctly
  • FEA results reflect parameter changes
  • Mesh regenerated properly
  • Expression import via .exp method works

Lessons Learned

[What did we learn that wasn't expected?]

Examples:

  • Optimizer consistently pushes beam thickness to maximum bounds
  • hole_count has minimal impact on displacement (< 5% variation)
  • Constraint appears infeasible with current design variable ranges
  • TPE sampler converges after ~30 trials

Visualization

[Link to generated plots if available]

Next Steps

[What should be done next based on these results?]

Examples:

  • → Substudy 05: Run refined search with narrowed parameter bounds
  • → Increase beam thickness limit to 80mm to meet constraint
  • → Perform sensitivity analysis to identify most important parameters
  • → Study complete - recommend design #43 for detailed validation

Files:

  • Configuration: config.json
  • Results: history.json, best_trial.json
  • Detailed Analysis: OPTIMIZATION_RESULTS.md (if significant findings)