Jae Yong Lee

According to our database, Jae Yong Lee authored at least 13 papers between 2020 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2026

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

J. Comput. Phys., 2026

2025

Finite Element Operator Network for Solving Elliptic-Type Parametric PDEs.

Jae Yong Lee, Seungchan Ko, Youngjoon Hong

SIAM J. Sci. Comput., 2025

Hamilton-Jacobi based policy-iteration via deep operator learning.

Jae Yong Lee, Yeoneung Kim

Neurocomputing, 2025

Physics-Informed Neural Operators for Tissue Elasticity Reconstruction.

Youjin Kim, Jae Yong Lee, Junseok Kwon

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2025, 2025

2024

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

Jinyoung Shin, Jae Yong Lee, Hyung Ju Hwang

Trans. Mach. Learn. Res., 2024

Error analysis for finite element operator learning methods for solving parametric second-order elliptic PDEs.

Youngjoon Hong, Seungchan Ko, Jae Yong Lee

CoRR, 2024

2023

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

J. Comput. Phys., May, 2023

Finite Element Operator Network for Solving Parametric PDEs.

Jae Yong Lee, Seungchan Ko, Youngjoon Hong

CoRR, 2023

HyperDeepONet: learning operator with complex target function space using the limited resources via hypernetwork.

Jae Yong Lee, Sung Woong Cho, Hyung Ju Hwang

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Pseudo-Differential Integral Operator for Learning Solution Operators of Partial Differential Equations.

Jinyoung Shin, Jae Yong Lee, Hyung Ju Hwang

CoRR, 2022

Solving PDE-Constrained Control Problems Using Operator Learning.

Rakhoon Hwang, Jae Yong Lee, Jinyoung Shin, Hyung Ju Hwang

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2020

Trend to equilibrium for the kinetic Fokker-Planck equation via the neural network approach.

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

J. Comput. Phys., 2020

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, Hyung Ju Hwang

CoRR, 2020