arXiv Machine Learning June 4, 2026 By Matthew Lowery, John Turnage, Zachary Morrow, John D. Jakeman, Akil Narayan, Shandian Zhe, Varun Shankar

Kernel Neural Operators (KNOs) for Scalable, Memory-efficient, Geometrically-flexible Operator Learning

Read the original on arXiv Machine Learning →

arXiv:2407. 00809v4 Announce Type: replace Abstract: This paper introduces the Kernel Neural Operator (KNO), a provably convergent operator-learning architecture that utilizes compositions of deep kernel-based integral operators for function-space approximation of operators (maps from functions to functions).

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Kernel Methods for Learning Operators with Multiple Inputs and Outputs

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