Publications
C: Conference, J: Journal, D : Domestic, P: Preprint / * Equal contribution
[P] In preparation
[P4] Conformal mapping Coordinates Physics-Informed Neural Networks (CoCo-PINNs): learning neural networks for designing neutral inclusions
Under review. arXiv:2501.07809 [cs.LG]
[P3] Hamilton-Jacobi Based Policy-Iteration via Deep Operator Learning
Jae Yong Lee, Yeoneung Kim
Under review. arXiv:2406.10920 [math.OC]
[P2] Error analysis for finite element operator learning methods for solving parametric second-order elliptic PDEs
Youngjoon Hong*, Seungchan Ko*, Jae Yong Lee*
Under review. arXiv:2404.17868 [math.NA]
[P1] 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]
[C4] Isometric Regularization for Manifolds of Functional Data (Project page)
Hyeongjun Heo, Seonghun Oh, Jae Yong Lee, Young Min Kim, Yonghyeon Lee
International Conference on Learning Representation (ICLR), 2025.
[J5] Finite Element Operator Network for Solving Elliptic-type parametric PDEs (Project page)
Jae Yong Lee, Seungchan Ko*, Youngjoon Hong*
To appear in SIAM Journal on Scientific Computing (SISC), 2025.
SungWoong Cho*, Jae Yong Lee*, Hyung Ju Hwang
ICLR 2024 Workshop on AI4DifferentialEquations In Science, 2024
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
Jae Yong Lee, Juhi Jang, Hyung Ju Hwang
Journal of Computational Physics (JCP), 2023.
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.
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.
Collaborators
Hyung Ju Hwang (POSTECH, South Korea)
Jin Woo Jang (POSTECH, South Korea)
Hyeontae Jo (Korea University, 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 (Konkuk University, South Korea)
Seungchan Ko (Inha University, South Korea)
Youngjoon Hong (Seoul National University, South Korea)
Young Min Kim (Seoul National University, South Korea)
Yonghyeon Lee (Massachusetts Institute of Technology(MIT), USA)
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)
Yeoneung Kim (Seoul National University of Science and Technology, South Korea)
Mikyoung Lim (KAIST, South Korea)
Daehee Cho (KAIST, South Korea)
Byungchan Lim (POSTECH, South Korea)
Yeonjong Shin (NC State University, USA)
Liu Liu (CUHK, Hong Kong)