40 citations · 85 across the 20 of their papers we have counts for
4 papers · 1 filter
Hamilton-Jacobi Based Policy-Iteration via Deep Operator Learning
Jae Yong Lee, Yeoneung Kim
The framework of deep operator network (DeepONet) has been widely exploited thanks to its capability of solving high dimensional partial differential equations. In this paper, we i…
Error analysis for finite element operator learning methods for solving parametric second-order elliptic PDEs
Youngjoon Hong, Seungchan Ko, Jaeyong Lee
In this paper, we provide a theoretical analysis of a type of operator learning method without data reliance based on the classical finite element approximation, which is called th…
Structure-Preserving Operator Learning: Modeling the Collision Operator of Kinetic Equations
Jae Yong Lee, Steffen Schotthöfer, Tianbai Xiao +2
This work explores the application of deep operator learning principles to a problem in statistical physics. Specifically, we consider the linear kinetic equation, consisting of a…
Learning time-dependent PDE via graph neural networks and deep operator network for robust accuracy on irregular grids
Sung Woong Cho, Jae Yong Lee, Hyung Ju Hwang
Scientific computing using deep learning has seen significant advancements in recent years. There has been growing interest in models that learn the operator from the parameters of…