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| Format: | Recurso digital |
| Sprache: | Englisch |
| Veröffentlicht: |
Zenodo
2025
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| Schlagworte: | |
| Online-Zugang: | https://doi.org/10.5281/zenodo.16791813 |
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Inhaltsangabe:
- <p>This repository contains the source code, model architectures, training/inference scripts, and environment files used in the manuscript <em>"</em><em>Transonic Aerodynamic </em><em>Predictions</em><em> with Sparse Edge-Augmented Transformers and Graph Attention Networks: A Comparative Study</em><em>"</em>, currently under peer review.</p> <p>It includes implementations of two models:</p> <ul> <li> <p>MeshGAT – a GAT-enhanced MeshGraphNet</p> </li> <li> <p>AeroFormer – a flow-adaptive sparse attention Edge-augmented Graph Transformer</p> </li> </ul> <p>The dataset structure, training logs, inference outputs, and environment specifications (YAML and requirements.txt) are provided to ensure reproducibility.</p> <p>This repository extends v1.0 with two key enhancements while preserving all original functionality: (1) K-fold cross-validation support via <code>Cross_Valid.py</code> for model's statistical robustness, generating per-fold performance metrics and checkpoints; (2) A new <code>Data_Utils.py</code> module that centralizes dataset handling (Mach/AoA parsing, DGL graph loading, and batching logic) to reduce code duplication. The update maintains backward compatibility with v1.0's dataset structure, model architectures (<code>MeshGAT</code> and <code>AeroFormer</code>), and environment specifications with improved reproducibility.</p> <p>*(v1.0 remains available at DOI: 10.5281/zenodo.15583112)*</p> <p>This upload is shared strictly for review purposes. Please contact the author for further clarifications.</p>