Papers & Preprints

C: Conference, J: Journal, D : Domestic, P: Preprint / * Equal contribution

[P] In preparation

[P2] Finite Element Operator Network for Solving Parametric PDEs

Jae Yong Lee, Seungchan Ko*, Youngjoon Hong*

Under review. arXiv:2308.04690 [math.NA]

[P1] Pseudo-Differential Integral Operator for Learning Solution Operators of Partial Differential Equations

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

Under review. arXiv:2201.11967 [cs.LG]

[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