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>
This commit is contained in:
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docs/08_ARCHIVE/historical/TODAY_PLAN_NOV18.md
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docs/08_ARCHIVE/historical/TODAY_PLAN_NOV18.md
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# Testing Plan - November 18, 2025
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**Goal**: Validate Hybrid Mode with real optimizations and verify centralized library system
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## Overview
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Today we're testing the newly refactored architecture with real-world optimizations. Focus is on:
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1. ✅ Hybrid Mode workflow (90% automation, no API key)
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2. ✅ Centralized extractor library (deduplication)
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3. ✅ Clean study folder structure
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4. ✅ Production readiness
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**Estimated Time**: 2-3 hours total
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---
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## Test 1: Verify Beam Optimization (30 minutes)
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### Goal
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Confirm existing beam optimization works with new architecture.
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### What We're Testing
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- ✅ Parameter bounds parsing (20-30mm not 0.2-1.0mm!)
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- ✅ Workflow config auto-saved
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- ✅ Extractors added to core library
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- ✅ Study manifest created (not code pollution)
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- ✅ Clean study folder structure
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### Steps
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#### 1. Review Existing Workflow JSON
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```bash
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# Open in VSCode
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code studies/simple_beam_optimization/1_setup/workflow_config.json
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```
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**Check**:
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- Design variable bounds are `[20, 30]` format (not `min`/`max`)
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- Extraction actions are clear (extract_mass, extract_displacement)
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- Objectives and constraints specified
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#### 2. Run Short Optimization (5 trials)
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```python
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# Create: studies/simple_beam_optimization/test_today.py
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from pathlib import Path
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from optimization_engine.llm_optimization_runner import LLMOptimizationRunner
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study_dir = Path("studies/simple_beam_optimization")
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workflow_json = study_dir / "1_setup/workflow_config.json"
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prt_file = study_dir / "1_setup/model/Beam.prt"
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sim_file = study_dir / "1_setup/model/Beam_sim1.sim"
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output_dir = study_dir / "2_substudies/test_nov18_verification"
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print("="*80)
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print("TEST 1: BEAM OPTIMIZATION VERIFICATION")
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print("="*80)
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print()
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print(f"Workflow: {workflow_json}")
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print(f"Model: {prt_file}")
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print(f"Output: {output_dir}")
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print()
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print("Running 5 trials to verify system...")
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print()
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runner = LLMOptimizationRunner(
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llm_workflow_file=workflow_json,
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prt_file=prt_file,
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sim_file=sim_file,
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output_dir=output_dir,
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n_trials=5 # Just 5 for verification
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)
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study = runner.run()
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print()
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print("="*80)
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print("TEST 1 RESULTS")
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print("="*80)
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print()
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print(f"Best design found:")
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print(f" beam_half_core_thickness: {study.best_params['beam_half_core_thickness']:.2f} mm")
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print(f" beam_face_thickness: {study.best_params['beam_face_thickness']:.2f} mm")
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print(f" holes_diameter: {study.best_params['holes_diameter']:.2f} mm")
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print(f" hole_count: {study.best_params['hole_count']}")
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print(f" Objective value: {study.best_value:.6f}")
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print()
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print("[SUCCESS] Optimization completed!")
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```
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Run it:
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```bash
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python studies/simple_beam_optimization/test_today.py
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```
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#### 3. Verify Results
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**Check output directory structure**:
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```bash
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# Should contain ONLY these files (no generated_extractors/!)
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dir studies\simple_beam_optimization\2_substudies\test_nov18_verification
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```
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**Expected**:
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```
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test_nov18_verification/
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├── extractors_manifest.json ✓ References to core library
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├── llm_workflow_config.json ✓ What LLM understood
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├── optimization_results.json ✓ Best design
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├── optimization_history.json ✓ All trials
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└── study.db ✓ Optuna database
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```
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**Check parameter values are realistic**:
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```python
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# Create: verify_results.py
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import json
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from pathlib import Path
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results_file = Path("studies/simple_beam_optimization/2_substudies/test_nov18_verification/optimization_results.json")
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with open(results_file) as f:
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results = json.load(f)
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print("Parameter values:")
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for param, value in results['best_params'].items():
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print(f" {param}: {value}")
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# VERIFY: thickness should be 20-30 range (not 0.2-1.0!)
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thickness = results['best_params']['beam_half_core_thickness']
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assert 20 <= thickness <= 30, f"FAIL: thickness {thickness} not in 20-30 range!"
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print()
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print("[OK] Parameter ranges are correct!")
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```
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**Check core library**:
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```python
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# Create: check_library.py
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from optimization_engine.extractor_library import ExtractorLibrary
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library = ExtractorLibrary()
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print(library.get_library_summary())
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```
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Expected output:
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```
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================================================================================
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ATOMIZER EXTRACTOR LIBRARY
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================================================================================
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Location: optimization_engine/extractors/
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Total extractors: 3
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Available Extractors:
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--------------------------------------------------------------------------------
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extract_mass
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Domain: result_extraction
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Description: Extract mass from FEA results
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File: extract_mass.py
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Signature: 2f58f241a96afb1f
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extract_displacement
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Domain: result_extraction
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Description: Extract displacement from FEA results
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File: extract_displacement.py
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Signature: 381739e9cada3a48
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extract_von_mises_stress
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Domain: result_extraction
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Description: Extract von Mises stress from FEA results
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File: extract_von_mises_stress.py
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Signature: 63d54f297f2403e4
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```
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### Success Criteria
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- ✅ Optimization completes without errors
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- ✅ Parameter values in correct range (20-30mm not 0.2-1.0mm)
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- ✅ Study folder clean (only 5 files, no generated_extractors/)
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- ✅ extractors_manifest.json exists
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- ✅ Core library contains 3 extractors
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- ✅ llm_workflow_config.json saved automatically
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### If It Fails
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- Check parameter bounds parsing in llm_optimization_runner.py:205-211
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- Verify NX expression names match workflow JSON
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- Check OP2 file contains expected results
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---
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## Test 2: Create New Optimization with Claude (1 hour)
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### Goal
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Use Claude Code to create a brand new optimization from scratch, demonstrating full Hybrid Mode workflow.
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### Scenario
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You have a cantilever plate that needs optimization:
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- **Design variables**: plate_thickness (3-8mm), support_width (20-50mm)
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- **Objective**: Minimize mass
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- **Constraints**: max_displacement < 1.5mm, max_stress < 150 MPa
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### Steps
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#### 1. Prepare Model (if you have one)
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```
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studies/
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cantilever_plate_optimization/
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1_setup/
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model/
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Plate.prt # Your NX model
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Plate_sim1.sim # Your FEM setup
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```
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**If you don't have a real model**, we'll simulate the workflow and use beam model as placeholder.
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#### 2. Describe Optimization to Claude
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Start conversation with Claude Code (this tool!):
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```
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YOU: I want to optimize a cantilever plate design.
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Design variables:
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- plate_thickness: 3 to 8 mm
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- support_width: 20 to 50 mm
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Objective:
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- Minimize mass
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Constraints:
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- Maximum displacement < 1.5 mm
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- Maximum von Mises stress < 150 MPa
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Can you help me create the workflow JSON for Hybrid Mode?
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```
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#### 3. Claude Creates Workflow JSON
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Claude (me!) will generate something like:
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```json
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{
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"study_name": "cantilever_plate_optimization",
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"optimization_request": "Minimize mass while keeping displacement < 1.5mm and stress < 150 MPa",
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"design_variables": [
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{
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"parameter": "plate_thickness",
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"bounds": [3, 8],
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"description": "Plate thickness in mm"
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},
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{
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"parameter": "support_width",
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"bounds": [20, 50],
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"description": "Support width in mm"
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}
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],
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"objectives": [
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{
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"name": "mass",
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"goal": "minimize",
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"weight": 1.0,
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"extraction": {
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"action": "extract_mass",
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"domain": "result_extraction",
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"params": {
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"result_type": "mass",
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"metric": "total"
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}
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}
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}
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],
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"constraints": [
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{
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"name": "max_displacement_limit",
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"type": "less_than",
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"threshold": 1.5,
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"extraction": {
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"action": "extract_displacement",
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"domain": "result_extraction",
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"params": {
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"result_type": "displacement",
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"metric": "max"
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}
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}
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},
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{
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"name": "max_stress_limit",
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"type": "less_than",
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"threshold": 150,
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"extraction": {
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"action": "extract_von_mises_stress",
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"domain": "result_extraction",
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"params": {
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"result_type": "stress",
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"metric": "max"
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}
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}
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}
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]
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}
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```
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#### 4. Save and Review
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```bash
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# Save to:
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# studies/cantilever_plate_optimization/1_setup/workflow_config.json
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# Review in VSCode
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code studies/cantilever_plate_optimization/1_setup/workflow_config.json
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```
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**Check**:
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- Parameter names match your NX expressions EXACTLY
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- Bounds in correct units (mm)
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- Extraction actions make sense for your model
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#### 5. Run Optimization
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```python
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# Create: studies/cantilever_plate_optimization/run_optimization.py
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from pathlib import Path
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from optimization_engine.llm_optimization_runner import LLMOptimizationRunner
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study_dir = Path("studies/cantilever_plate_optimization")
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workflow_json = study_dir / "1_setup/workflow_config.json"
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prt_file = study_dir / "1_setup/model/Plate.prt"
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sim_file = study_dir / "1_setup/model/Plate_sim1.sim"
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output_dir = study_dir / "2_substudies/optimization_run_001"
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print("="*80)
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print("TEST 2: NEW CANTILEVER PLATE OPTIMIZATION")
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print("="*80)
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print()
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print("This demonstrates Hybrid Mode workflow:")
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print(" 1. You described optimization in natural language")
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print(" 2. Claude created workflow JSON")
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print(" 3. LLMOptimizationRunner does 90% automation")
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print()
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print("Running 10 trials...")
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print()
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runner = LLMOptimizationRunner(
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llm_workflow_file=workflow_json,
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prt_file=prt_file,
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sim_file=sim_file,
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output_dir=output_dir,
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n_trials=10
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)
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study = runner.run()
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print()
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print("="*80)
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print("TEST 2 RESULTS")
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print("="*80)
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print()
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print(f"Best design found:")
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for param, value in study.best_params.items():
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print(f" {param}: {value:.2f}")
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print(f" Objective: {study.best_value:.6f}")
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print()
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print("[SUCCESS] New optimization from scratch!")
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```
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Run it:
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```bash
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python studies/cantilever_plate_optimization/run_optimization.py
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```
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#### 6. Verify Library Reuse
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**Key test**: Did it reuse extractors from Test 1?
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```python
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# Create: check_reuse.py
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from optimization_engine.extractor_library import ExtractorLibrary
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from pathlib import Path
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import json
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library = ExtractorLibrary()
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# Check manifest from Test 2
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manifest_file = Path("studies/cantilever_plate_optimization/2_substudies/optimization_run_001/extractors_manifest.json")
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with open(manifest_file) as f:
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manifest = json.load(f)
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print("Extractors used in Test 2:")
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for sig in manifest['extractors_used']:
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info = library.get_extractor_metadata(sig)
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print(f" {info['name']} (signature: {sig})")
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print()
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print("Core library status:")
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print(f" Total extractors: {len(library.catalog)}")
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print()
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# VERIFY: Should still be 3 extractors (reused from Test 1!)
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assert len(library.catalog) == 3, "FAIL: Should reuse extractors, not duplicate!"
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print("[OK] Extractors were reused from core library!")
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print("[OK] No duplicate code generated!")
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```
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### Success Criteria
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- ✅ Claude successfully creates workflow JSON from natural language
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- ✅ Optimization runs without errors
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- ✅ Core library STILL only has 3 extractors (reused!)
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- ✅ Study folder clean (no generated_extractors/)
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- ✅ Results make engineering sense
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### If It Fails
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- NX expression mismatch: Check Tools → Expression in NX
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- OP2 results missing: Verify FEM setup outputs required results
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- Library issues: Check `optimization_engine/extractors/catalog.json`
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---
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## Test 3: Validate Extractor Deduplication (15 minutes)
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### Goal
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Explicitly test that signature-based deduplication works correctly.
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### Steps
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||||
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#### 1. Run Same Workflow Twice
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```python
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# Create: test_deduplication.py
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||||
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||||
from pathlib import Path
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from optimization_engine.llm_optimization_runner import LLMOptimizationRunner
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from optimization_engine.extractor_library import ExtractorLibrary
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print("="*80)
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print("TEST 3: EXTRACTOR DEDUPLICATION")
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print("="*80)
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print()
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library = ExtractorLibrary()
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print(f"Core library before test: {len(library.catalog)} extractors")
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print()
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# Run 1: First optimization
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print("RUN 1: First optimization with displacement extractor...")
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study_dir = Path("studies/simple_beam_optimization")
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runner1 = LLMOptimizationRunner(
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llm_workflow_file=study_dir / "1_setup/workflow_config.json",
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prt_file=study_dir / "1_setup/model/Beam.prt",
|
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sim_file=study_dir / "1_setup/model/Beam_sim1.sim",
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output_dir=study_dir / "2_substudies/dedup_test_run1",
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n_trials=2 # Just 2 trials
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)
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study1 = runner1.run()
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print("[OK] Run 1 complete")
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||||
print()
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||||
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||||
# Check library
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library = ExtractorLibrary() # Reload
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count_after_run1 = len(library.catalog)
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print(f"Core library after Run 1: {count_after_run1} extractors")
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print()
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||||
# Run 2: Same workflow, different output directory
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print("RUN 2: Same optimization, different study...")
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runner2 = LLMOptimizationRunner(
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llm_workflow_file=study_dir / "1_setup/workflow_config.json",
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||||
prt_file=study_dir / "1_setup/model/Beam.prt",
|
||||
sim_file=study_dir / "1_setup/model/Beam_sim1.sim",
|
||||
output_dir=study_dir / "2_substudies/dedup_test_run2",
|
||||
n_trials=2 # Just 2 trials
|
||||
)
|
||||
study2 = runner2.run()
|
||||
print("[OK] Run 2 complete")
|
||||
print()
|
||||
|
||||
# Check library again
|
||||
library = ExtractorLibrary() # Reload
|
||||
count_after_run2 = len(library.catalog)
|
||||
print(f"Core library after Run 2: {count_after_run2} extractors")
|
||||
print()
|
||||
|
||||
# VERIFY: Should be same count (deduplication worked!)
|
||||
print("="*80)
|
||||
print("DEDUPLICATION TEST RESULTS")
|
||||
print("="*80)
|
||||
print()
|
||||
if count_after_run1 == count_after_run2:
|
||||
print(f"[SUCCESS] Extractor count unchanged ({count_after_run1} → {count_after_run2})")
|
||||
print("[SUCCESS] Deduplication working correctly!")
|
||||
print()
|
||||
print("This means:")
|
||||
print(" ✓ Run 2 reused extractors from Run 1")
|
||||
print(" ✓ No duplicate code generated")
|
||||
print(" ✓ Core library stays clean")
|
||||
else:
|
||||
print(f"[FAIL] Extractor count changed ({count_after_run1} → {count_after_run2})")
|
||||
print("[FAIL] Deduplication not working!")
|
||||
|
||||
print()
|
||||
print("="*80)
|
||||
```
|
||||
|
||||
Run it:
|
||||
```bash
|
||||
python test_deduplication.py
|
||||
```
|
||||
|
||||
#### 2. Inspect Manifests
|
||||
|
||||
```python
|
||||
# Create: compare_manifests.py
|
||||
|
||||
import json
|
||||
from pathlib import Path
|
||||
|
||||
manifest1 = Path("studies/simple_beam_optimization/2_substudies/dedup_test_run1/extractors_manifest.json")
|
||||
manifest2 = Path("studies/simple_beam_optimization/2_substudies/dedup_test_run2/extractors_manifest.json")
|
||||
|
||||
with open(manifest1) as f:
|
||||
data1 = json.load(f)
|
||||
|
||||
with open(manifest2) as f:
|
||||
data2 = json.load(f)
|
||||
|
||||
print("Run 1 used extractors:")
|
||||
for sig in data1['extractors_used']:
|
||||
print(f" {sig}")
|
||||
|
||||
print()
|
||||
print("Run 2 used extractors:")
|
||||
for sig in data2['extractors_used']:
|
||||
print(f" {sig}")
|
||||
|
||||
print()
|
||||
if data1['extractors_used'] == data2['extractors_used']:
|
||||
print("[OK] Same extractors referenced")
|
||||
print("[OK] Signatures match correctly")
|
||||
else:
|
||||
print("[WARN] Different extractors used")
|
||||
```
|
||||
|
||||
### Success Criteria
|
||||
- ✅ Core library size unchanged after Run 2
|
||||
- ✅ Both manifests reference same extractor signatures
|
||||
- ✅ No duplicate extractor files created
|
||||
- ✅ Study folders both clean (only manifests, no code)
|
||||
|
||||
### If It Fails
|
||||
- Check signature computation in `extractor_library.py:73-92`
|
||||
- Verify catalog.json persistence
|
||||
- Check `get_or_create()` logic in `extractor_library.py:93-137`
|
||||
|
||||
---
|
||||
|
||||
## Test 4: Dashboard Visualization (30 minutes) - OPTIONAL
|
||||
|
||||
### Goal
|
||||
Verify dashboard can visualize the optimization results.
|
||||
|
||||
### Steps
|
||||
|
||||
#### 1. Start Dashboard
|
||||
```bash
|
||||
cd dashboard/api
|
||||
python app.py
|
||||
```
|
||||
|
||||
#### 2. Open Browser
|
||||
```
|
||||
http://localhost:5000
|
||||
```
|
||||
|
||||
#### 3. Load Study
|
||||
- Navigate to beam optimization study
|
||||
- View optimization history plot
|
||||
- Check Pareto front (if multi-objective)
|
||||
- Inspect trial details
|
||||
|
||||
### Success Criteria
|
||||
- ✅ Dashboard loads without errors
|
||||
- ✅ Can select study from dropdown
|
||||
- ✅ History plot shows all trials
|
||||
- ✅ Best design highlighted
|
||||
- ✅ Can inspect individual trials
|
||||
|
||||
---
|
||||
|
||||
## Summary Checklist
|
||||
|
||||
At end of testing session, verify:
|
||||
|
||||
### Architecture
|
||||
- [ ] Core library system working (deduplication verified)
|
||||
- [ ] Study folders clean (only 5 files, no code pollution)
|
||||
- [ ] Extractors manifest created correctly
|
||||
- [ ] Workflow config auto-saved
|
||||
|
||||
### Functionality
|
||||
- [ ] Parameter bounds parsed correctly (actual mm values)
|
||||
- [ ] Extractors auto-generated successfully
|
||||
- [ ] Optimization completes without errors
|
||||
- [ ] Results make engineering sense
|
||||
|
||||
### Hybrid Mode Workflow
|
||||
- [ ] Claude successfully creates workflow JSON from natural language
|
||||
- [ ] LLMOptimizationRunner handles workflow correctly
|
||||
- [ ] 90% automation achieved (only JSON creation manual)
|
||||
- [ ] Full audit trail saved (workflow config + manifest)
|
||||
|
||||
### Production Readiness
|
||||
- [ ] No code duplication across studies
|
||||
- [ ] Clean folder structure maintained
|
||||
- [ ] Library grows intelligently (deduplication)
|
||||
- [ ] Reproducible (workflow config captures everything)
|
||||
|
||||
---
|
||||
|
||||
## If Everything Passes
|
||||
|
||||
**Congratulations!** 🎉
|
||||
|
||||
You now have a production-ready optimization system with:
|
||||
- ✅ 90% automation (Hybrid Mode)
|
||||
- ✅ Clean architecture (centralized library)
|
||||
- ✅ Full transparency (audit trails)
|
||||
- ✅ Code reuse (deduplication)
|
||||
- ✅ Professional structure (studies = data, core = code)
|
||||
|
||||
### Next Steps
|
||||
1. Run longer optimizations (50-100 trials)
|
||||
2. Try real engineering problems
|
||||
3. Build up core library with domain-specific extractors
|
||||
4. Consider upgrading to Full LLM Mode (API) when ready
|
||||
|
||||
### Share Your Success
|
||||
- Update DEVELOPMENT.md with test results
|
||||
- Document any issues encountered
|
||||
- Add your own optimization examples to `studies/`
|
||||
|
||||
---
|
||||
|
||||
## If Something Fails
|
||||
|
||||
### Debugging Strategy
|
||||
|
||||
1. **Check logs**: Look for error messages in terminal output
|
||||
2. **Verify files**: Ensure NX model and sim files exist and are valid
|
||||
3. **Inspect manifests**: Check `extractors_manifest.json` is created
|
||||
4. **Review library**: Run `python -m optimization_engine.extractor_library` to see library status
|
||||
5. **Test components**: Run E2E test: `python tests/test_phase_3_2_e2e.py`
|
||||
|
||||
### Common Issues
|
||||
|
||||
**"Expression not found"**:
|
||||
- Open NX model
|
||||
- Tools → Expression
|
||||
- Verify exact parameter names
|
||||
- Update workflow JSON
|
||||
|
||||
**"No mass results"**:
|
||||
- Check OP2 file contains mass data
|
||||
- Try different result type (displacement, stress)
|
||||
- Verify FEM setup outputs required results
|
||||
|
||||
**"Extractor generation failed"**:
|
||||
- Check pyNastran can read OP2: `python -c "from pyNastran.op2.op2 import OP2; OP2().read_op2('path')"`
|
||||
- Review knowledge base patterns
|
||||
- Manually create extractor if needed
|
||||
|
||||
**"Deduplication not working"**:
|
||||
- Check `optimization_engine/extractors/catalog.json`
|
||||
- Verify signature computation
|
||||
- Review `get_or_create()` logic
|
||||
|
||||
### Get Help
|
||||
- Review `docs/HYBRID_MODE_GUIDE.md`
|
||||
- Check `docs/ARCHITECTURE_REFACTOR_NOV17.md`
|
||||
- Inspect code in `optimization_engine/llm_optimization_runner.py`
|
||||
|
||||
---
|
||||
|
||||
**Ready to revolutionize your optimization workflow!** 🚀
|
||||
|
||||
**Start Time**: ___________
|
||||
**End Time**: ___________
|
||||
**Tests Passed**: ___ / 4
|
||||
**Issues Found**: ___________
|
||||
**Notes**: ___________
|
||||
Reference in New Issue
Block a user