Regularity for the Vlasov-Poisson System in a Convex Domain. - Hyung Ju Hwang (SIAM J. Math. Anal., 2004)

Global Solutions of Nonlinear Transport Equations for Chemosensitive Movement. - Hyung Ju Hwang; Kyungkeun Kang; Angela Stevens (SIAM J. Math. Anal., 2005)

Erratum: "Global Solutions of Nonlinear Transport Equations for Chemosensitive Movement" [SIAM J. Math. Analysis 36 (2005) 1177-1199]. - Hyung Ju Hwang; Kyungkeun Kang; Angela Stevens (SIAM J. Math. Anal., 2007)

Capillary oscillations at a circular orifice. - Hyeon Jeong Kim; Marco A. Fontelos; Hyung Ju Hwang (Appl. Math. Lett., 2013)

Stationary Solutions of the Vlasov-Poisson System with Diffusive Boundary Conditions. - Emre Esentürk; Hyung Ju Hwang; Walter A. Strauss (J. Nonlinear Sci., 2015)

Stability, Instability, and Bifurcation in Electrified Thin Films. - Marco A. Fontelos; Hyung Ju Hwang; Youngmin Oh (SIAM J. Math. Anal., 2016)

Local Stability and Performance of Simple Gradient Penalty mu-Wasserstein GAN. - Cheolhyeong Kim; Seungtae Park; Hyung Ju Hwang (CoRR, 2018)

The Fokker-Planck Equation with Absorbing Boundary Conditions in Bounded Domains. - Hyung Ju Hwang; Juhi Jang; Jaewoo Jung (SIAM J. Math. Anal., 2018)

Data analytic approach for bankruptcy prediction. - Hwijae Son; C. Hyun; Du Phan; Hyung Ju Hwang (Expert Syst. Appl., 2019)

Hybrid Model of Mathematical and Neural Network Formulations for Rolling Force and Temperature Prediction in Hot Rolling Processes. - Rakhoon Hwang; Hyeontae Jo; Kyung Seok Kim; Hyung Ju Hwang (IEEE Access, 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 002; Jin Woo Jang; Hyung Ju Hwang (CoRR, 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 002 (J. Comput. Phys., 2020)

Deep neural network approach to forward-inverse problems. - Hyeontae Jo; Hwijae Son; Hyung Ju Hwang; Eun Heui Kim (Networks Heterog. Media, 2020)

Posting Bot Detection on Blockchain-based Social Media Platform using Machine Learning Techniques. - Taehyun Kim; Hyomin Shin; Hyung Ju Hwang; Seungwon Jeong (ICWSM, 2021)

Explainability of Machine Learning Models for Bankruptcy Prediction. - Min Sue Park; Hwijae Son; Chongseok Hyun; Hyung Ju Hwang (IEEE Access, 2021)

Sobolev Training for the Neural Network Solutions of PDEs. - Hwijae Son; Jin Woo Jang; Woo Jin Han; Hyung Ju Hwang (CoRR, 2021)

Lagrangian dual framework for conservative neural network solutions of kinetic equations. - Hyung Ju Hwang; Hwijae Son (CoRR, 2021)

Development of patients triage algorithm from nationwide COVID-19 registry data based on machine learning. - Hyung Ju Hwang; Seyoung Jung; Min Sue Park; Hyeontae Jo (CoRR, 2021)

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

Solving PDE-Constrained Control Problems Using Operator Learning. - Rakhoon Hwang; Jae Yong Lee 002; Jinyoung Shin; Hyung Ju Hwang (AAAI, 2022)

Pseudo-Differential Integral Operator for Learning Solution Operators of Partial Differential Equations. - Jinyoung Shin; Jae Yong Lee 002; Hyung Ju Hwang (CoRR, 2022)

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

Prior preference learning from experts: Designing a reward with active inference. - Jinyoung Shin; Cheolhyeong Kim; Hyung Ju Hwang (Neurocomputing, 2022)

Option compatible reward inverse reinforcement learning. - Rakhoon Hwang; Hanjin Lee; Hyung Ju Hwang (Pattern Recognit. Lett., 2022)

NEAR: Neighborhood Edge AggregatoR for Graph Classification. - Cheolhyeong Kim; Haeseong Moon; Hyung Ju Hwang (ACM Trans. Intell. Syst. Technol., 2022)

Local Stability of Wasserstein GANs With Abstract Gradient Penalty. - Cheolhyeong Kim; Seungtae Park; Hyung Ju Hwang (IEEE Trans. Neural Networks Learn. Syst., 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)

Anomaly Detection in Time Series Data and its Application to Semiconductor Manufacturing. - Rakhoon Hwang; Seungtae Park; Youngwook Bin; Hyung Ju Hwang (IEEE Access, 2023)

Physics-Informed Neural Networks for Microprocessor Thermal Management Model. - Hwijae Son; Hyunwoo Cho; Hyung Ju Hwang (IEEE Access, 2023)

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

opPINN: Physics-informed neural network with operator learning to approximate solutions to the Fokker-Planck-Landau equation. - Jae Yong Lee 002; Juhi Jang; Hyung Ju Hwang (J. Comput. Phys., 2023)

The deep minimizing movement scheme. - Min Sue Park; Cheolhyeong Kim; Hwijae Son; Hyung Ju Hwang (J. Comput. Phys., 2023)

Concept-Oriented Self-Explaining Neural Networks. - Min Sue Park; Hyung Ju Hwang (Neural Process. Lett., 2023)

Solving PDEs on Point Clouds by Physics-Informed Learning with Graph Neural Networks. - Rakhoon Hwang; Junseung Ryu; Seungtae Park; Hyung Ju Hwang (ECAI, 2024)

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)

Sobolev Training for Operator Learning. - Namkyeong Cho; Junseung Ryu; 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)

Density physics-informed neural networks reveal sources of cell heterogeneity in signal transduction. - Hyeontae Jo; Hyukpyo Hong; Hyung Ju Hwang; Won Chang; Jae Kyoung Kim (Patterns, 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)

Pseudo-Differential Neural Operator: Generalize Fourier Neural operator for Learning Solution Operators of Partial Differential Equations. - Jinyoung Shin; Jae Yong Lee 002; Hyung Ju Hwang (Trans. Mach. Learn. Res., 2024)

A Neural Operator Unifying Graph Neural Networks and Point Transformers. - Junseung Ryu; Namkyeong Cho; Hyung Ju Hwang (IEEE Access, 2025)

Self-Supervised Drift-Resilient Classification for Time Series Industrial Anomaly Detection. - Myung-Kyo Seo; Byeong Hoon Yoon; Junseung Ryu; Hyung Ju Hwang (IEEE Access, 2025)

Enhancing Fourier Neural Operators with CNNs architectures: Pooling, groupwise convolution and inverted block. - Seungtae Park; Heejoon Jeon; Hyung Ju Hwang (Neurocomputing, 2025)

A Physics-Informed Neural Network framework for solving PDEs on point clouds via surface reconstruction. - Junseung Ryu; Seungtae Park; Hyung Ju Hwang (Neural Networks, 2025)

FourierSpecNet: Neural Collision Operator Approximation Inspired by the Fourier Spectral Method for Solving the Boltzmann Equation. - Jae Yong Lee; Gwang Jae Jung; Byung Chan Lim; Hyung Ju Hwang (CoRR, 2025)

Learning from Imperfect Data: Robust Inference of Dynamic Systems using Simulation-based Generative Model. - Hyunwoo Cho; Hyeontae Jo; Hyung Ju Hwang (CoRR, 2025)