Hybrid exact-learned equivariant operator for incompressible Stokes flow fixes the known core kernel exactly and learns only the boundary correction as a second-kind operator, achieving high accuracy, data efficiency, and cross-shape generalization.
Geometric generalization of neural operators from kernel integral perspective.arXiv preprint arXiv:2602.01498, 2026
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LiNO introduces a light-transport-inspired decomposition of neural operator latent dynamics into pointwise reflection/refraction and input-dependent scattering for improved structure in operator learning.
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Solver Exactness, Learned Flexibility: Equivariant Boundary-Correction Operators for Stokes Flow
Hybrid exact-learned equivariant operator for incompressible Stokes flow fixes the known core kernel exactly and learns only the boundary correction as a second-kind operator, achieving high accuracy, data efficiency, and cross-shape generalization.
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Let There Be Light: Reflection, Refraction and Scattering for Neural Operators
LiNO introduces a light-transport-inspired decomposition of neural operator latent dynamics into pointwise reflection/refraction and input-dependent scattering for improved structure in operator learning.