2 papers
cs.LG2026
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…
math.NA2026
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…