Machine Learning and Scientific Computing (MLSC) LAB
Publications
Articles Submitted
[S5] J. Lee , S. Ko, and Y. Hong, Error analysis for finite element operator learning methods for solving parametric second-order elliptic PDEs, submitted
[S4] G.-M. Gie, Y. Hong, C.-Y. Jung, and D. Lee, Singular layer physics-informed neural network method for convection-dominated boundary layer problems in two dimensions, submitted
[S3] J. Oh, S.-Y. Cho, S.-B. Yun, E. Park, and Y. Hong, Separable physics-informed neural networks for solving the BGK Model of the Boltzmann equation, submitted
[S2] Y. Hong, S. Gray, and D. Nicholls, Building epsilon-near-zero materials from layered uniaxial metamaterials, submitted
[S1] S. Ko, S.-B. Yun, and Y. Hong, Convergence analysis of unsupervised Legendre-Galerkin neural networks for second-order elliptic PDEs, submitted
Articles Published or Accepted
[48] J. Lee , S. Ko, and Y. Hong, Finite Element Operator Network for Solving Parametric PDEs, SIAM Journal on Scientific Computing, 2024
[47] D. Park, S. Lee, S. Kim, T. Lee, Y. Hong, and H. Kim, Constant Acceleration Flow, NeurIPS, 2024
[46] N. Kim, D. Lee, C. Kim, D. Lee, and Y. Hong, Simple arithmetic operation in latent space can generate a novel three dimensional graph metamaterials, NPJ Computational Materials, 2024
[45] J. Bona, H. Chen, Y. Hong, M. Panthee, and M. Scialom, The effect of higher-order dissipation on solutions of the generalized Korteweg-de Vries equation, Communications in Contemporary Mathematics, 2024
[44] S. Kim, H. Yang, Y. Kim, Y. Hong, and E. Park, Hydra: Multi-head low-rank adaptation for parameter efficient fine-tuning, Neural Networks, 2024
[43] S. Kim, J. Kim, H. Sul, and Y. Hong, An Adaptive dual-level reinforcement learning approach for optimal trade execution, Expert Systems With Applications, 2024
[42] Y. Seol, S. Kim, M. Jung, and Y. Hong, A novel physics-aware graph networks using high-order numerical methods in weather forecasting model, Knowledge-Based Systems, 2024
[41] J. Bona, H. Chen, Y. Hong, M. Panthee, and M. Scialom, The long wavelength limit of periodic solutions of water wave models, Studies in Applied Mathematics, 2024
[40] G.-M. Gie, Y. Hong, C.-Y. Jung, and T. Munkhjin, Semi-analytic PINN methods for boundary layer problems in a rectangular domain, Journal of Computational and Applied Mathematics, 2024
[39] T.-Y. Chang, G.-M. Gie, Y. Hong, and C.-Y. Jung, Singular layer physics informed neural network method for plane parallel flows, Computers and Mathematics with Applications, 2024
[38] J. Choi, T. Yun, N. Kim, and Y. Hong, Spectral Operator Learning for Parametric PDEs Without Data Reliance, Computer Methods in Applied Mechanics and Engineering, 2024
[37] G.-M. Gie, Y. Hong, and C.-Y. Jung, Semi-analytic PINN methods for singularly perturbed boundary value problems, Applicable Analysis, 2024
[36] S. Kim, S.-B. Yun, H.-O. Bae, M. Lee, and Y. Hong, Physics-Informed Convolutional Transformer for predicting Volatility Surface, Quantitative Finance, 2024
[35] J. Cho, S. Nam, H. Yang, S.-B. Yun, Y. Hong, and E. Park, Separable Physics-Informed Neural Networks, NeurIPS 2023, (Spotlight!)
[34] B. Chudomelka, Y. Hong, J. Morgan, H. Kim, and J. Park, Deep neural network for solving differential equations motivated by Legendre-Galerkin approximation, International Journal of Numerical Analysis and Modeling, 2023
[33] J. Choi, N. Kim, and Y. Hong, Unsupervised Legendre-Galerkin Neural Network for Solving Partial Differential Equations, IEEE Access, 2023
[32] N. Kim, D. Lee, and Y. Hong, Data-efficient deep generative model with discrete latent representations for high-fidelity digital materials, ACS Materials Letters, 2023
[31] N. Kang, B. Lee, Y. Hong, S. Yun, and E. Park, PIXEL: Physics-Informed Cell Representations for Fast and Accurate PDE Solvers, AAAI, 2022
[30] B. Ahn, C. Kim, Y. Hong, and H. Kim, Invertible Monotone Operators for Normalizing Flows, Neural Information Processing Systems (Neurips), 2022
[29] Y. Hong and D. P. Nicholls, Data-driven design of thin-film optical systems using deep active learning, Optics Express, 2022.
[28] Y. Hong and J. Bona, Numerical study of the generalized Korteweg-de Vries equations with oscillating nonlinearities and boundary conditions, Water Waves, 2022.
[27] A. Bousquet, W. Conrad, O. Sadat, N. Vardanyan, and Y. Hong, Deep learning simulation of the SIRD model of Covid-19 with vaccination effects, Scientific Reports, 2022.
[26] J. Choi, Y. Hong, C.-Y. Jung, and H. Lee, Viscosity approximation of the solution to Burgers' equations with shock layers, Applicable Analysis, 2022.
[25] H. Heo, D. Ko, J. Lee, Y. Hong, and H. Kim, Search-and-Attack: Temporally sparse adversarial perturbations on videos, IEEE Access, Vol. 9, 2021.
[24] H.-M. Woo, Y. Hong, B. Kwon, and B.-J Yoon, Accelerating optimal experimental design for robust synchronization of uncertain Kuramoto oscillator model using machine learning, IEEE Transactions on Signal Processing, Vol. 69, 2021
[23] Y. Hong and D. P. Nicholls, On the consistent choice of effective permittivity and effective conductivity for modeling graphene, Journal of the Optical Society of America A, Vol. 38, 2021.
[22] Y. Hong, B. Kwon and B.-J. Yoon, Optimal experimental design for uncertain systems based on coupled differential equations, IEEE Access, Vol. 9, 2021
[21] B. Kim, B. Chudomelka, J. Park, J. Kang, Y. Hong, H. Kim, Robust neural networks inspired by strong stability preserving Runge-Kutta methods, European Conference on Computer Vision, 2021.
[20] Y. Hong and D. P. Nicholls, A rigorous numerical analysis of the transformed field expansion method for diffraction by periodic and layered structures, SIAM Journal on Numerical Analysis, 59(1), 2021.
[19] M. Chekroun, Y. Hong, and R. Temam, Enriched numerical scheme for singularly perturbed Quasi-Geostrophic equations, Journal of Computational Physics, Vol. 416, 2020.
[18] A. Bousquet, Y. Hong, R. Temam, and J. Tribbia, Numerical simulations of inviscid hydrostatic primitive equations of humid atmosphere above a mountain, Journal of Scientific Computing, Vol. 83, 2020.
[17] Y. Hong, M. Otten, M. Min, S. Gray, and D. Nicholls, Periodic Corrugations to Increase Efficiency of Thermophotovoltaic Emitting Structures, Applied Physics Letters, Vol. 114 (5), 2019.
[16] J. Bona, H. Chen, Y. Hong, and O. Karakashian, Numerical study of the second-order correct Hamiltonian model for unidirectional water waves, Water Waves, Vol. 1, 3-40, 2019.
[15] Y. Hong and D. P. Nicholls, A High-Order Perturbation of Surfaces Method for Vector Electromagnetic Scattering by Doubly Layered Periodic Crossed Gratings, Journal of Computational Physics, Vol. 372, 748-772, 2018.
[14] Y. Hong, C.-Y. Jung, and R. Temam, Boundary layer analysis for the stochastic nonlinear reaction-diffusion equations, Physica D, Vol. 376–377, no. 1 pp. 247-258, 2018.
[13] Y. Hong and D. P. Nicholls, A high-order perturbation of surfaces method for scattering of linear waves by periodic multiply layered gratings in two and three dimensions, Journal of Computational Physics, Vol. 345, no. 15, pp. 162-188, 2017.
[12] Y. Hong and C.-Y. Jung, Enriched spectral method for stiff convection-dominated equations, Journal of Scientific Computing, Vol. 74, no. 3, pp 1325–1346, 2018.
[11] Y. Hong and D. P. Nicholls, A stable high-order perturbation of surfaces method for numerical simulation of diffraction problems in triply layered media, Journal of Computational Physics, Vol. 330, no. 1, pp. 1043-1068, 2017.
[10] D. Bouche, Y. Hong, and C.-Y. Jung, Asymptotic analysis of the scattering problem for the Helmholtz equations with high wave numbers, Discrete and Continuous Dynamical Systems - Series A, Vol. 37, no. 3, pp. 1159-1181, 2017.
[9] Y. Hong, Global attractor of atmospheric equations, Asymptotic Analysis, vol. 96, no. 2, pp. 91-107, 2016.
[8] A. Bousquet, M. Chekroun, Y. Hong, R. Temam, and J. Tribbia, Numerical simulations of the humid atmosphere above a mountain, Mathematics of Climate and Weather Forecasting, vol. 1, no.1, 96-126, 2015.
[7] Y. Hong and D. Wirosoetisno, Timestepping schemes for the 3d Navier-Stokes equations, Applied Numerical Mathematics, Vol. 96 (2015), 153-164.
[6] Y. Hong, C.-Y. Jung, and R. Temam, Singular perturbation analysis of time dependent convection-diffusion equations in a circle, Nonlinear Analysis: Theory, Methods & Applications, Vol. 119 (2015), 127-148.
[5] Y. Hong, Numerical approximation of the singularly perturbed heat equation in a circle, Journal of Scientific Computing, Vol. 62 (2015), no. 1, 1-24.
[4] A. Bousquet, G.-M. Gie, Y. Hong, and J. Laminie, A higher order finite volume resolution method for a system related to the inviscid primitive equations in a complex domain, Numerische Mathematik, Vol. 128 (2014), no. 3, 431-461.
[3] Y. Hong, C.-Y. Jung, and R. Temam, On the numerical approximations of stiff convection-diffusion equations in a circle, Numerische Mathematik, Vol. 127 (2014), no. 2, 291-313.
[2] Y. Hong, C.-Y. Jung, and J. Laminie, Singularly perturbed reaction-diffusion equations in a circle with numerical applications, International Journal of Computer Mathematics, Vol. 90 (2013), no. 11, 2308-2325.
[1] Q. Chen, Y. Hong, and R. Temam, Analysis of a penalty method, Journal of Scientific Computing, Vol. 53 (2012), no. 1, 3-34.