Publications

C: Conference, J: Journal, D : Domestic, P: Preprint

[P] In preparation

[P5] FourierSpecNet: Neural Collision Operator Approximation Inspired by the Fourier Spectral Method for Solving the Boltzmann Equation

Jae Yong Lee, Gwang Jae Jung*, Byungchan Lim*, Hyung Ju Hwang

Under review. arXiv:2504.20408 [math.NA]

[P4] Deep learning-based moment closure for multi-phase computation of semiclassical limit of the Schrödinger equation

Jin Woo Jang*, Jae Yong Lee*, Liu Liu*, Zhenyi Zhu*

Under review. arXiv:2504.08341 [math.NA]

[P3] Conformal mapping Coordinates Physics-Informed Neural Networks (CoCo-PINNs): learning neural networks for designing neutral inclusions

Daehee Cho, Hyeonmin Yun, Jaeyong Lee*, Mikyoung Lim*

Preprint. arXiv:2501.07809 [cs.LG]

[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**

Preprint. arXiv:2402.16613 [math.NA]

[C5] Physics-Informed Neural Operators for Tissue Elasticity Reconstruction

Youjin Kim, Jae Yong Lee*, Junseok Kwon*

Medical Image Computing and Computer Assisted Intervention (MICCAI), 2025 [Provisional accept: ~9%].

[J6] Hamilton-Jacobi Based Policy-Iteration via Deep Operator Learning

Jae Yong Lee, Yeoneung Kim

Neurocomputing, 2025.

[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*

SIAM Journal on Scientific Computing (SISC), 2025.

[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.