| On the numerical approximations of stiff convection–diffusion equations in a circle Y Hong, CY Jung, R Temam Numerische Mathematik 127 (2), 291-313, 2014 | 34 | 2014 |
| Deep learning forecasting using time-varying parameters of the SIRD model for Covid-19 A Bousquet, WH Conrad, SO Sadat, N Vardanyan, Y Hong Scientific Reports 12 (1), 3030, 2022 | 24 | 2022 |
| Singularly perturbed reaction–diffusion equations in a circle with numerical applications Y Hong, CY Jung, J Laminie International Journal of Computer Mathematics 90 (11), 2308-2325, 2013 | 16 | 2013 |
| A high-order perturbation of surfaces method for scattering of linear waves by periodic multiply layered gratings in two and three dimensions Y Hong, DP Nicholls Journal of Computational Physics 345, 162-188, 2017 | 15 | 2017 |
| Separable PINN: Mitigating the curse of dimensionality in physics-informed neural networks J Cho, S Nam, H Yang, SB Yun, Y Hong, E Park arXiv preprint arXiv:2211.08761, 2022 | 14 | 2022 |
| Optimal experimental design for uncertain systems based on coupled differential equations Y Hong, B Kwon, BJ Yoon IEEE Access 9, 53804-53810, 2021 | 9 | 2021 |
| A high-order perturbation of surfaces method for vector electromagnetic scattering by doubly layered periodic crossed gratings Y Hong, DP Nicholls Journal of Computational Physics 372, 748-772, 2018 | 9 | 2018 |
| A stable high-order perturbation of surfaces method for numerical simulation of diffraction problems in triply layered media Y Hong, DP Nicholls Journal of Computational Physics 330, 1043-1068, 2017 | 9 | 2017 |
| Separable physics-informed neural networks J Cho, S Nam, H Yang, SB Yun, Y Hong, E Park Advances in Neural Information Processing Systems 36, 2024 | 7 | 2024 |
| Enriched numerical scheme for singularly perturbed barotropic quasi-geostrophic equations MD Chekroun, Y Hong, RM Temam Journal of Computational Physics 416, 109493, 2020 | 7 | 2020 |
| Numerical simulations of the humid atmosphere above a mountain A Bousquet, MD Chekroun, Y Hong, RM Temam, J Tribbia Mathematics of Climate and Weather Forecasting 1 (1), 2015 | 7 | 2015 |
| A higher order finite volume resolution method for a system related to the inviscid primitive equations in a complex domain A Bousquet, GM Gie, Y Hong, J Laminie Numerische Mathematik 128, 431-461, 2014 | 7 | 2014 |
| Robust neural networks inspired by strong stability preserving Runge-Kutta methods B Kim, B Chudomelka, J Park, J Kang, Y Hong, HJ Kim Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 6 | 2020 |
| Enriched spectral method for stiff convection-dominated equations Y Hong, CY Jung Journal of Scientific Computing 74, 1325-1346, 2018 | 6 | 2018 |
| Singular perturbation analysis of time dependent convection–diffusion equations in a circle Y Hong, CY Jung, R Temam Nonlinear Analysis: Theory, Methods & Applications 119, 127-148, 2015 | 6 | 2015 |
| Unsupervised Legendre–Galerkin Neural Network for Solving Partial Differential Equations J Choi, N Kim, Y Hong IEEE Access 11, 23433-23446, 2023 | 5 | 2023 |
| Data-efficient deep generative model with discrete latent representation for high-fidelity digital materials N Kim, D Lee, Y Hong ACS Materials Letters 5 (3), 730-737, 2023 | 5 | 2023 |
| Data-driven design of thin-film optical systems using deep active learning Y Hong, DP Nicholls Optics Express 30 (13), 22901-22910, 2022 | 5 | 2022 |
| Accelerating optimal experimental design for robust synchronization of uncertain Kuramoto oscillator model using machine learning HM Woo, Y Hong, B Kwon, BJ Yoon IEEE Transactions on Signal Processing 69, 6473-6487, 2021 | 5 | 2021 |
| Deep neural network for solving differential equations motivated by Legendre-Galerkin approximation B Chudomelka, Y Hong, H Kim, J Park arXiv preprint arXiv:2010.12975, 2020 | 5 | 2020 |
