cfd

GM is leveraging AI and machine learning to collapse traditional engineering phases into a single probabilistic method, reducing simulation times from hours to minutes and accelerating vehicle development.

This paper investigates the role of group-equivariant architectures in neural fluid dynamics surrogates, introducing the AB-GATr model. It finds that equivariance is beneficial when data lacks strong alignment, but can degrade performance on highly aligned datasets.

This paper presents AI CFD Scientist, an open-source AI agent for computational fluid dynamics that autonomously discovers physics corrections using vision-language verification and code modification, outperforming general AI scientists on CFD tasks.

This paper introduces AeroJEPA, a Joint-Embedding Predictive Architecture for scalable 3D aerodynamic field modeling. It addresses limitations in current surrogate models by predicting semantic latent representations of flow fields, enabling efficient high-fidelity analysis and design optimization.