Fast rotational equivariance for physics GNNs: The source code for our paper on Rotational Equivariant GraphNets via local Eigenbases is now available: https://github.com/tum-pbs/strain-base-gnn
* We introduce a basis-transformation approach (SB-GNNs) that preserves full geometric and physical information while avoiding the heavy computational cost of conventional equivariant layers.
* Across three challenging PDE benchmarks, our method matches SOTA accuracy, consistently outperforms data augmentation, and delivers order-of-magnitude efficiency gains in practice.
* If you’re interested in ML for PDEs, CFD, or equivariant learning, the code and test cases are ready to use and build upon. Let us know how it works for you!
Please check out the full Physics-of-Fluids paper here: https://pubs.aip.org/aip/pof/article/37/8/087178/3359552/Rotational-equivariant-graph-neural-networks-via
