Euclidean Fourier Neural Operators
Fourier neural operators (FNOs) provide an efficient framework for learning mappings between function spaces as they are, by construction, independent of the grid resolution at which they are trained and evaluated. However, FNOs are not independent of the periodic domain they are applied to: their discrete spectral weights are indexed by integer Fourier mode numbers, which correspond to physical wavevectors. When applied to a different domain, the same trained weights act at different wavevectors, and the FNO silently represents a different operator. This makes FNOs unsuitable for tasks where transfer across domains is crucial. We propose Euclidean Fourier neural operators~(EFNOs) as a do...
| Authors | Nathanael Bosch, Niklas Frederik Schmitz, Michael F. Herbst |
|---|---|
| Primary category | cs.LG |
| Published | 28 August 2026 |
| Last updated at source | 5 September 2026 |
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