Signals 4 · free daily AI digest

Euclidean Fourier Neural Operators

Paper recorded by Signals 4 on 2026-08-28 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-08-28 on arXiv · recorded by Signals 4 on 2026-08-31

Category: cs.LG · 机器学习 · first seen 2026-08-31

Abstract

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 w

Read on arXiv →

#211 most recent of 215 cs.LG papers we have recorded · ↑ newer: Curvature-Conditioned Multiscale Momentum with Sphere Constraints for · ↓ older: SymboLLM-FE: LLM-Accelerated Symbolic Regression for Automated Feature
Cite this page: Euclidean Fourier Neural Operators: the #211 most recent of 215 cs.LG papers we have recorded (as of 2026-08-28). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/euclidean-fourier-neural-operators.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
Related: More cs.LG papers · arXiv signals · All papers · Today in AI
Get 4 AI signals a day by email — free.
Subscribe free → See all plans →
Get 4 AI signals a day by email — free
All models · All repos · By company · Daily editions