Advancing Aerodynamic Modeling with AI: A Multi-fidelity Dataset and GNN Surrogates
Published:Dec 24, 2025 04:53
•1 min read
•ArXiv
Analysis
This research explores the application of Graph Neural Networks (GNNs) for creating surrogate models of aerodynamic fields. The paper's contribution lies in the development of a novel dataset and empirical scaling laws, potentially accelerating design cycles.
Key Takeaways
- •Develops a novel multi-fidelity dataset for aerodynamic simulations.
- •Applies Graph Neural Networks (GNNs) for surrogate modeling of complex aerodynamic fields.
- •Investigates empirical scaling laws to improve the efficiency and accuracy of the surrogate models.
Reference
“The research focuses on a 'Multi-fidelity Double-Delta Wing Dataset' and its application to GNN-based aerodynamic field surrogates.”