When Does Equivariance Help? Canonical Alignment in Neural Fluid Surrogates
This work tested whether enforcing group-equivariant architectures improves generalization in neural fluid simulations by examining how problem alignment affects performance. The findings show that equivariance is beneficial when problems lack strong data regularities, but it can be detrimental when the dataset strongly aligns with the problem's symmetry. The authors propose a new architecture, AB-GATr, to handle both scalability and symmetry.