Atomizer/atomizer-field/TESTING_CHECKLIST.md

AtomizerField Testing Checklist

Quick reference for testing status and next steps.

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✅ Completed Tests

Environment Setup

• Conda environment created (atomizer_field)
• All dependencies installed
• NumPy MINGW-W64 issue resolved
• No segmentation faults

Smoke Tests (5/5)

• Model creation (128,589 parameters)
• Forward pass
• Loss functions (4 types)
• Batch processing
• Gradient flow

Simple Beam Test (7/7)

• File existence (BDF + OP2)
• Directory setup
• Module imports
• BDF/OP2 parsing (5,179 nodes, 4,866 elements)
• Data validation
• Graph conversion
• Neural prediction (95.94 ms)

Visualization

• 3D mesh rendering
• Displacement field (original + deformed)
• Stress field (von Mises)
• Report generation (markdown + images)

Unit Validation

• UNITSYS detection (MN-MM)
• Material properties (E = 200 GPa)
• Stress values (117 MPa reasonable)
• Force values (2.73 MN validated)
• Direction vectors preserved

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❌ Not Yet Tested (Requires Trained Model)

Physics Tests (0/4)

• Cantilever beam (analytical comparison)
• Equilibrium check (∇·σ + f = 0)
• Constitutive law (σ = C:ε)
• Energy conservation

Learning Tests (0/4)

• Memorization (single case < 1% error)
• Interpolation (between cases < 10% error)
• Extrapolation (unseen loads < 20% error)
• Pattern recognition (physics transfer)

Integration Tests (0/5)

• Batch prediction
• Gradient computation
• Optimization loop
• Uncertainty quantification
• Online learning

Performance Tests (0/3)

• Accuracy benchmark (< 10% error)
• Speed benchmark (< 50 ms)
• Scalability (10K+ nodes)

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🔧 Known Issues to Fix

Minor (Non-blocking)

• Unit labels: "MPa" should be "kPa" (or convert values)
• Missing SPCs warning (investigate BDF)
• Unicode encoding (mostly fixed, minor cleanup remains)

Documentation

• Unit conversion guide
• Training data generation guide
• User manual

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🚀 Testing Roadmap

Phase 1: Pre-Training Validation

Status: ✅ COMPLETE

• Core pipeline working
• Test case validated
• Units understood
• Visualization working

Phase 2: Training Preparation

Status: 🔜 NEXT

• Fix unit labels (30 min)
• Document unit system (1 hour)
• Create training data generation script
• Generate 50 test cases (1-2 weeks)

Phase 3: Initial Training

Status: ⏸️ WAITING

• Train on 50 cases (2-4 hours)
• Validate on 10 held-out cases
• Check loss convergence
• Run memorization test

Phase 4: Physics Validation

Status: ⏸️ WAITING

• Cantilever beam test
• Equilibrium check
• Energy conservation
• Compare vs analytical solutions

Phase 5: Full Validation

Status: ⏸️ WAITING

• Run full test suite (18 tests)
• Accuracy benchmarks
• Speed benchmarks
• Scalability tests

Phase 6: Production Deployment

Status: ⏸️ WAITING

• Integration with Atomizer
• End-to-end optimization test
• Performance profiling
• User acceptance testing

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📊 Test Commands Quick Reference

Run Tests

# Activate environment
conda activate atomizer_field

# Quick smoke tests (30 seconds)
python test_suite.py --quick

# Simple Beam end-to-end (1 minute)
python test_simple_beam.py

# Physics tests (15 minutes) - REQUIRES TRAINED MODEL
python test_suite.py --physics

# Full test suite (1 hour) - REQUIRES TRAINED MODEL
python test_suite.py --full

Visualization

# Mesh only
python visualize_results.py test_case_beam --mesh

# Displacement
python visualize_results.py test_case_beam --displacement

# Stress
python visualize_results.py test_case_beam --stress

# Full report
python visualize_results.py test_case_beam --report

Unit Validation

# Check parsed data units
python check_units.py

# Check OP2 raw data
python check_op2_units.py

# Check actual values
python check_actual_values.py

Training (When Ready)

# Generate training data
python batch_parser.py --input Models/ --output training_data/

# Train model
python train.py \
  --data_dirs training_data/* \
  --epochs 100 \
  --batch_size 16 \
  --loss physics

# Monitor training
tensorboard --logdir runs/

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📈 Success Criteria

Phase 1: Core System ✅

• All smoke tests passing
• End-to-end test passing
• Real FEA data processed
• Visualization working

Phase 2: Training Ready 🔜

• Unit labels correct
• 50+ training cases generated
• Training script validated
• Monitoring setup (TensorBoard)

Phase 3: Model Trained ⏸️

• Training loss < 0.01
• Validation loss < 0.05
• No overfitting (train ≈ val loss)
• Predictions physically reasonable

Phase 4: Physics Validated ⏸️

• Equilibrium error < 1%
• Constitutive error < 5%
• Energy conservation < 5%
• Analytical test < 5% error

Phase 5: Production Ready ⏸️

• Prediction error < 10%
• Inference time < 50 ms
• All 18 tests passing
• Integration with Atomizer working

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🎯 Current Focus

Status: ✅ Core validation complete, ready for training phase

Next immediate steps:

1. Fix unit labels (optional, 30 min)
2. Generate training data (critical, 1-2 weeks)
3. Train model (critical, 2-4 hours)

Blockers: None - system ready!

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📞 Quick Status Check

Run this to verify system health:

conda activate atomizer_field
python test_simple_beam.py

Expected output:

TEST 1: Files exist ✓
TEST 2: Directory setup ✓
TEST 3: Modules import ✓
TEST 4: BDF/OP2 parsed ✓
TEST 5: Data validated ✓
TEST 6: Graph created ✓
TEST 7: Prediction made ✓

[SUCCESS] All 7 tests passed!

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Testing Checklist v1.0 Last updated: November 24, 2025 Status: Phase 1 complete, Phase 2 ready to start