Graph-Based Neural Models for Transonic Aerodynamics: AeroFormer & MeshGAT Architectures
Fuente:
Zenodo
Salvato in:
| Autore principale: | |
|---|---|
| Natura: | Recurso digital |
| Lingua: | inglese |
| Pubblicazione: |
Zenodo
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866901613083361280 |
|---|---|
| author | Hussain, Saad |
| author_facet | Hussain, Saad |
| contents | <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> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_16791813 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Graph-Based Neural Models for Transonic Aerodynamics: AeroFormer & MeshGAT Architectures Hussain, Saad Machine Learning Computational fluid dynamics <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> |
| title | Graph-Based Neural Models for Transonic Aerodynamics: AeroFormer & MeshGAT Architectures |
| topic | Machine Learning Computational fluid dynamics |
| url | https://doi.org/10.5281/zenodo.16791813 |