[P] In preparation

[P3] Error analysis for finite element operator learning methods for solving parametric second-order elliptic PDEs

Youngjoon Hong*, Seungchan Ko*, Jae Yong Lee*

Submitted. arXiv:2404.17868 [math.NA]

[P2] Structure-Preserving Operator Learning: Modeling the Collision Operator of Kinetic Equations

Jae Yong Lee*, Steffen Schotthöfer*, Tianbai Xiao*, Sebastian Krumscheid, Martin Frank

Submitted. arXiv:2402.16613 [math.NA]

[P1] Finite Element Operator Network for Solving Parametric PDEs

Jae Yong Lee, Seungchan Ko*, Youngjoon Hong*

Submitted. arXiv:2308.04690 [math.NA]

[C3] Learning time-dependent PDE via graph neural networks and deep operator network for robust accuracy on irregular grids

SungWoong Cho*, Jae Yong Lee*, Hyung Ju Hwang

ICLR 2024 Workshop on AI4DifferentialEquations In Science, 2024

[J4] Pseudo-Differential Neural Operator: Generalize Fourier Neural operator for Learning Solution Operators of Partial Differential Equations

Jin Young Shin*, Jae Yong Lee*, Hyung Ju Hwang

Transactions on Machine Learning Research (TMLR), 2024,

[D1] 물리 정보 기계학습 의 발전 및 응용

Jae Yong Lee* and Hwijae Son*

전자공학회지 제50권 6호, 2023

[J3] opPINN: Physics-Informed Neural Network with operator learning to approximate solutions to the Fokker-Planck-Landau equation

Jae Yong Lee, Juhi Jang, Hyung Ju Hwang

Journal of Computational Physics (JCP), 2023.

[C2] HyperDeepONet: learning operator with complex target function space using the limited resources via hypernetwork

Jae Yong Lee*, SungWoong Cho*, Hyung Ju Hwang

International Conference on Learning Representation (ICLR), 2023

[C1] Solving PDE-constrained Control Problems using Operator Learning

Rakhoon Hwang*, Jae Yong Lee*, Jin Young Shin*, Hyung Ju Hwang

Association for the Advancement of Artificial Intelligence (AAAI), 2022

[J2] The model reduction of the Vlasov-Poisson-Fokker-Planck system to the Poisson-Nernst-Planck system via the Deep Neural Network Approach

Jae Yong Lee, Jin Woo Jang, and Hyung Ju Hwang

ESAIM: Mathematical Modelling and Numerical Analysis (ESAIM: M2AN), 2021

[J1] Trend to Equilibrium for the Kinetic Fokker-Planck Equation via the Neural Network Approach

Hyung Ju Hwang*, Jin Woo Jang*, Hyeontae Jo*, and Jae Yong Lee*

Journal of Computational Physics (JCP), 2020

On the Neural Network approach to the Vlasov-Poisson-Fokker-Planck system and its diffusion limit

• Hyung Ju Hwang (POSTECH, South Korea)

• Jin Woo Jang (POSTECH, South Korea)

• Hyeontae Jo (IBS, South Korea)

• Rakhoon Hwang (Hyundai Motor Company, South Korea)

• Jin Young Shin (Samsung Advanced Institute of Technology, South Korea)

• SungWoong Cho (SAARC, South Korea)

• Juhi Jang (University of Southern California, USA)

• Hwijae Son (Hanbat National University, South Korea)

• Seungchan Ko (Inha University, South Korea)

• Youngjoon Hong (KAIST, South Korea)

• Steffen Schotthöfer (Oak Ridge National Laboratory, USA)

• Tianbai Xiao (University of Chinese Academy of Sciences, China)

• Sebastian Krumscheid (Karlsruhe Institute of Technology, German)

• Martin Frank (Karlsruhe Institute of Technology, German)

• Byungchan Lim (POSTECH, South Korea)

• Yeonjong Shin (NC State University, USA)

• Yeoneung Kim (Seoul National University of Science and Technology, South Korea)

• Liu Liu (CUHK, Hong Kong)