Traveling Wave Solutions of Partial Differential Equations Via Neural Networks. - Sung Woong Cho; Hyung Ju Hwang; Hwijae Son (J. Sci. Comput., 2021)

AL-PINNs: Augmented Lagrangian relaxation method for Physics-Informed Neural Networks. - Hwijae Son; Sung Woong Cho; Hyung Ju Hwang (CoRR, 2022)

HyperDeepONet: learning operator with complex target function space using the limited resources via hypernetwork. - Jae Yong Lee 002; Sung Woong Cho; Hyung Ju Hwang (ICLR, 2023)

Enhanced physics-informed neural networks with Augmented Lagrangian relaxation method (AL-PINNs). - Hwijae Son; Sung Woong Cho; Hyung Ju Hwang (Neurocomputing, 2023)

Learning time-dependent PDE via graph neural networks and deep operator network for robust accuracy on irregular grids. - Sung Woong Cho; Jae Yong Lee 002; Hyung Ju Hwang (CoRR, 2024)

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model. - Hyunwoo Cho; Sung Woong Cho; Hyeontae Jo; Hyung Ju Hwang (CoRR, 2024)

Estimating the distribution of parameters in differential equations with repeated cross-sectional data. - Hyeontae Jo; Sung Woong Cho; Hyung Ju Hwang (PLoS Comput. Biol., 2024)

Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems. - Sung Woong Cho; Hwijae Son (ICLR, 2025)

Toward a Robust and Generalizable Metamaterial Foundation Model. - Namjung Kim; Dongseok Lee; Jongbin Yu; Sung Woong Cho; Dosung Lee; Yesol Park; Youngjoon Hong (CoRR, 2025)