6 citations · 6 across the 5 of their papers we have counts for
3 papers · 1 filter
Kernel Neural Operators (KNOs) for Scalable, Memory-efficient, Geometrically-flexible Operator Learning
Matthew Lowery, John Turnage, Zachary Morrow +4
This paper introduces the Kernel Neural Operator (KNO), a provably convergent operator-learning architecture that utilizes compositions of deep kernel-based integral operators for…
SUPN: Shallow Universal Polynomial Networks
Zachary Morrow, Michael Penwarden, Brian Chen +3
Deep neural networks (DNNs) and Kolmogorov-Arnold networks (KANs) are popular methods for function approximation due to their flexibility and expressivity. However, they typically…
Arbitrarily-Conditioned Multi-Functional Diffusion for Multi-Physics Emulation
Da Long, Zhitong Xu, Guang Yang +2
Modern physics simulation often involves multiple functions of interests, and traditional numerical approaches are known to be complex and computationally costly. While machine lea…