AI RESEARCH
Physics-Aligned Canonical Equivariant Fourier Neural Operator under Symmetry-Induced Shifts
arXiv CS.LG
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ArXi:2605.18606v1 Announce Type: new Neural operators approximate PDE solution maps, but they need not respect the symmetries of the governing equation. In out-of-distribution (OOD) regimes, a standard neural operator must often learn coordinate alignment and physical evolution within a single map, which can hurt generalization. We use known continuous symmetries of evolution equations on periodic domains to separate these two roles.