3 citations · 4 across the 6 of their papers we have counts for
4 papers · 1 filter
Robust training and rigorous error analysis of physics-informed neural networks for the -Laplace equation
Kyueon Choi, Seungchan Ko, Dohyun Kwon
With the rise of scientific machine learning, physics-informed neural networks (PINNs) have been extensively applied to a wide range of problems. Nevertheless, most theoretical ana…
Sobolev Approximation of Deep ReLU Networks in Log-Barron Space
Changhoon Song, Seungchan Ko, Youngjoon Hong
Universal approximation theorems show that neural networks can approximate any continuous function; however, the number of parameters may grow exponentially with the ambient dimens…
Data-Free Asymptotics-Informed Operator Networks for Singularly Perturbed PDEs
Jinsil Lee, Youngjoon Hong, Seungchan Ko +1
Recent advances in machine learning (ML) have opened new possibilities for solving partial differential equations (PDEs), yet robust performance in challenging regimes remains limi…
Sparse FEONet: A Low-Cost, Memory-Efficient Operator Network via Finite-Element Local Sparsity for Parametric PDEs
Seungchan Ko, Jiyeon Kim, Dongwook Shin
In this paper, we study the finite element operator network (FEONet), an operator-learning method for parametric problems, originally introduced in J. Y. Lee, S. Ko, and Y. Hong, F…