AI RESEARCH
Simple yet Effective: Low-Rank Spatial Attention for Neural Operators
arXiv CS.LG
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ArXi:2604.03582v1 Announce Type: new Neural operators have emerged as data-driven surrogates for solving partial differential equations (PDEs), and their success hinges on efficiently modeling the long-range, global coupling among spatial points induced by the underlying physics. In many PDE regimes, the induced global interaction kernels are empirically compressible, exhibiting rapid spectral decay that admits low-rank approximations.