AI RESEARCH
EqGINO: Equivariant Geometry-Informed Fourier Neural Operators for 3D PDEs
arXiv CS.LG
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ArXi:2606.03260v1 Announce Type: new Deep learning surrogates for 3D Partial Differential Equations (PDEs) often fail to generalize across geometric transformations because they depend heavily on specific coordinate systems. While equivariant networks offer a solution, they typically rely on local operations in the spatial domain, making the global receptive field, which is essential for PDE dynamics, computationally expensive.