Analysis of a Penalty Method. - Qingshan Chen; Youngjoon Hong; Roger Temam (J. Sci. Comput., 2012)

Singularly perturbed reaction-diffusion equations in a circle with numerical applications. - Youngjoon Hong; Chang-Yeol Jung; Jacques Laminie (Int. J. Comput. Math., 2013)

A higher order Finite Volume resolution method for a system related to the inviscid primitive equations in a complex domain. - Arthur Bousquet; Gung-Min Gie; Youngjoon Hong; Jacques Laminie (Numerische Mathematik, 2014)

On the numerical approximations of stiff convection-diffusion equations in a circle. - Youngjoon Hong; Chang-Yeol Jung; Roger Temam (Numerische Mathematik, 2014)

Numerical Approximation of the Singularly Perturbed Heat Equation in a Circle. - Youngjoon Hong (J. Sci. Comput., 2015)

Global attractor of atmospheric equations. - Youngjoon Hong (Asymptot. Anal., 2016)

A stable high-order perturbation of surfaces method for numerical simulation of diffraction problems in triply layered media. - Youngjoon Hong; David P. Nicholls (J. Comput. Phys., 2017)

A high-order perturbation of surfaces method for scattering of linear waves by periodic multiply layered gratings in two and three dimensions. - Youngjoon Hong; David P. Nicholls (J. Comput. Phys., 2017)

A high-order perturbation of surfaces method for vector electromagnetic scattering by doubly layered periodic crossed gratings. - Youngjoon Hong; David P. Nicholls (J. Comput. Phys., 2018)

Enriched Spectral Method for Stiff Convection-Dominated Equations. - Youngjoon Hong; Chang-Yeol Jung (J. Sci. Comput., 2018)

Robust Neural Networks Inspired by Strong Stability Preserving Runge-Kutta Methods. - Byungjoo Kim; Bryce Chudomelka; Jinyoung Park; Jaewoo Kang; Youngjoon Hong; Hyunwoo J. Kim (ECCV (9), 2020)

Deep neural network for solving differential equations motivated by Legendre-Galerkin approximation. - Bryce Chudomelka; Youngjoon Hong; Hyunwoo J. Kim; Jinyoung Park (CoRR, 2020)

Enriched numerical scheme for singularly perturbed barotropic Quasi-Geostrophic equations. - Mickaël D. Chekroun; Youngjoon Hong; Roger Temam (J. Comput. Phys., 2020)

Numerical Simulations of the Two-Dimensional Inviscid Hydrostatic Primitive Equations with Humidity and Saturation. - Arthur Bousquet; Youngjoon Hong; Roger Temam; Joseph J. Tribbia (J. Sci. Comput., 2020)

Search-and-Attack: Temporally Sparse Adversarial Perturbations on Videos. - Hwan Heo; Dohwan Ko; Jaewon Lee; Youngjoon Hong; Hyunwoo J. Kim (IEEE Access, 2021)

Optimal Experimental Design for Uncertain Systems Based on Coupled Differential Equations. - Youngjoon Hong; Bongsuk Kwon; Byung-Jun Yoon (IEEE Access, 2021)

A Rigorous Numerical Analysis of the Transformed Field Expansion Method for Diffraction by Periodic, Layered Structures. - Youngjoon Hong; David P. Nicholls (SIAM J. Numer. Anal., 2021)

Accelerating Optimal Experimental Design for Robust Synchronization of Uncertain Kuramoto Oscillator Model Using Machine Learning. - Hyun-Myung Woo; Youngjoon Hong; Bongsuk Kwon; Byung-Jun Yoon (IEEE Trans. Signal Process., 2021)

Invertible Monotone Operators for Normalizing Flows. - Byeongkeun Ahn; Chiyoon Kim; Youngjoon Hong; Hyunwoo J. Kim (NeurIPS, 2022)

Unsupervised Legendre-Galerkin Neural Network for Stiff Partial Differential Equations. - Junho Choi; Namjung Kim; Youngjoon Hong (CoRR, 2022)

Semi-analytic PINN methods for singularly perturbed boundary value problems. - Gung-Min Gie; Youngjoon Hong; Chang-Yeol Jung (CoRR, 2022)

Separable PINN: Mitigating the Curse of Dimensionality in Physics-Informed Neural Networks. - Junwoo Cho; Seungtae Nam; Hyunmo Yang; Seok-Bae Yun; Youngjoon Hong; Eunbyung Park (CoRR, 2022)

Convergence analysis of unsupervised Legendre-Galerkin neural networks for linear second-order elliptic PDEs. - Seungchan Ko; Seok-Bae Yun; Youngjoon Hong (CoRR, 2022)

PIXEL: Physics-Informed Cell Representations for Fast and Accurate PDE Solvers. - Namgyu Kang; Byeonghyeon Lee; Youngjoon Hong; Seok-Bae Yun; Eunbyung Park (AAAI, 2023)

Separable Physics-Informed Neural Networks. - Junwoo Cho; Seungtae Nam; Hyunmo Yang; Seok-Bae Yun; Youngjoon Hong; Eunbyung Park (NeurIPS, 2023)

Unsupervised Legendre-Galerkin Neural Network for Solving Partial Differential Equations. - Junho Choi; Namjung Kim; Youngjoon Hong (IEEE Access, 2023)

Finite Element Operator Network for Solving Parametric PDEs. - Jae Yong Lee 002; Seungchan Ko; Youngjoon Hong (CoRR, 2023)

Hydra: Multi-head Low-rank Adaptation for Parameter Efficient Fine-tuning. - Sanghyeon Kim; Hyunmo Yang; Younghyun Kim; Youngjoon Hong; Eunbyung Park (CoRR, 2023)

Spectral operator learning for parametric PDEs without data reliance. - Junho Choi; Taehyun Yun; Namjung Kim; Youngjoon Hong (CoRR, 2023)

Semi-analytic physics informed neural network for convection-dominated boundary layer problems in 2D. - Gung-Min Gie; Youngjoon Hong; Chang-Yeol Jung; Dongseok Lee (CoRR, 2023)

Constant Acceleration Flow. - Dogyun Park; Sojin Lee; Sihyeon Kim; Taehoon Lee 004; Youngjoon Hong; Hyunwoo J. Kim (NeurIPS, 2024)

Singular layer physics informed neural network method for plane parallel flows. - Teng-Yuan Chang; Gung-Min Gie; Youngjoon Hong; Chang-Yeol Jung (Comput. Math. Appl., 2024)

Error analysis for finite element operator learning methods for solving parametric second-order elliptic PDEs. - Youngjoon Hong; Seungchan Ko; Jae Yong Lee 002 (CoRR, 2024)

MaD-Scientist: AI-based Scientist solving Convection-Diffusion-Reaction Equations Using Massive PINN-Based Prior Data. - Mingu Kang; Dongseok Lee; Woojin Cho; Jaehyeon Park; Kookjin Lee; Anthony Gruber; Youngjoon Hong; Noseong Park (CoRR, 2024)

Singular layer PINN methods for Burgers' equation. - Teng-Yuan Chang; Gung-Min Gie; Youngjoon Hong; Chang-Yeol Jung (CoRR, 2024)

Forward and Inverse Simulation of Pseudo-Two-Dimensional Model of Lithium-Ion Batteries Using Neural Networks. - Myeong-Su Lee; Jaemin Oh; Dong-chan Lee; KangWook Lee; Sooncheol Park; Youngjoon Hong (CoRR, 2024)

An adaptive dual-level reinforcement learning approach for optimal trade execution. - Soohan Kim; Jimyeong Kim; Hong Kee Sul; Youngjoon Hong (Expert Syst. Appl., 2024)

Semi-analytic PINN methods for boundary layer problems in a rectangular domain. - Gung-Min Gie; Youngjoon Hong; Chang-Yeol Jung; Tselmuun Munkhjin (J. Comput. Appl. Math., 2024)

A novel physics-aware graph network using high-order numerical methods in weather forecasting model. - Yunchang Seol; Suho Kim; Minwoo Jung; Youngjoon Hong (Knowl. Based Syst., 2024)

Hydra: Multi-head low-rank adaptation for parameter efficient fine-tuning. - Sanghyeon Kim; Hyunmo Yang; Yunghyun Kim; Youngjoon Hong; Eunbyung Park (Neural Networks, 2024)

PIG: Physics-Informed Gaussians as Adaptive Parametric Mesh Representations. - Namgyu Kang; Jaemin Oh; Youngjoon Hong; Eunbyung Park (ICLR, 2025)

Physically interpretable discrete latent representations for the design of advanced mechanical metamaterials in complex geometries. - Hansom Choi; Youngjoon Hong; Namjung Kim (Eng. Appl. Artif. Intell., 2025)

Corrigendum to "Hydra: Multi-head Low-rank Adaptation for Parameter Efficient Fine-tuning" [Neural Networks Volume 178, October (2024), 1-11/106414]]. - Sanghyeon Kim; Hyunmo Yang; Younghyun Kim; Youngjoon Hong; Eunbyung Park (Neural Networks, 2025)

Finite Element Operator Network for Solving Elliptic-Type Parametric PDEs. - Jae Yong Lee 002; Seungchan Ko; Youngjoon Hong (SIAM J. Sci. Comput., 2025)

Separable Physics-Informed Neural Networks for Solving the BGK Model of the Boltzmann Equation. - Jaemin Oh; Seung-Yeon Cho; Seok-Bae Yun; Eunbyung Park; Youngjoon Hong (SIAM J. Sci. Comput., 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)

Singular layer physics-informed neural network method for convection-dominated boundary layer problems in two dimensions. - Gung-Min Gie; Youngjoon Hong; Chang-Yeol Jung; Dongseok Lee (J. Comput. Appl. Math., 2026)