Atomizer/atomizer_field_training_data/uav_arm_test/README.md

AtomizerField Training Data

Study Name: uav_arm_atomizerfield_test Generated: 2025-11-25 12:01:15

Directory Structure

uav_arm_test/
├── trial_0001/
│   ├── input/
│   │   └── model.bdf      # NX Nastran input deck (BDF format)
│   ├── output/
│   │   └── model.op2      # NX Nastran binary results (OP2 format)
│   └── metadata.json      # Design parameters, objectives, constraints
├── trial_0002/
│   └── ...
├── study_summary.json     # Overall study metadata
└── README.md              # This file

Design Variables

• beam_half_core_thickness
• beam_face_thickness
• holes_diameter
• hole_count

Objectives

• mass
• fundamental_frequency

Constraints

• max_displacement_limit
• max_stress_limit
• min_frequency_limit

Usage with AtomizerField

1. Parse Training Data

cd Atomizer-Field
python batch_parser.py --data-dir "C:\Users\antoi\Documents\Atomaste\Atomizer\atomizer_field_training_data\uav_arm_test"

This converts BDF/OP2 files to PyTorch Geometric format.

2. Validate Parsed Data

python validate_parsed_data.py

3. Train Neural Network

python train.py --data-dir "training_data/parsed/" --epochs 200

4. Use Trained Model in Atomizer

cd ../Atomizer
python run_optimization.py --config studies/uav_arm_atomizerfield_test/workflow_config.json --use-neural

File Formats

• BDF (.bdf): Nastran Bulk Data File - contains mesh, materials, loads, BCs
• OP2 (.op2): Nastran Output2 - binary results with displacements, stresses, etc.
• metadata.json: Human-readable trial metadata

AtomizerField Documentation

See Atomizer-Field/docs/ for complete documentation on:

• Neural network architecture
• Training procedures
• Integration with Atomizer
• Uncertainty quantification

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Generated by Atomizer Training Data Exporter