Functional Attention replaces pairwise softmax attention with structured linear operators inspired by geometric functional maps to produce compact, resolution-invariant representations for operator learning.
HT-net: Hierarchical transformer based operator learning model for multiscale PDEs
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Transolver learns intrinsic physical states from discretized meshes by adaptively splitting domains into flexible learnable slices and computing attention over physics-aware tokens, achieving state-of-the-art PDE solving on general geometries.
citing papers explorer
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Functional Attention: From Pairwise Affinities to Functional Correspondences
Functional Attention replaces pairwise softmax attention with structured linear operators inspired by geometric functional maps to produce compact, resolution-invariant representations for operator learning.
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Transolver: A Fast Transformer Solver for PDEs on General Geometries
Transolver learns intrinsic physical states from discretized meshes by adaptively splitting domains into flexible learnable slices and computing attention over physics-aware tokens, achieving state-of-the-art PDE solving on general geometries.