Seungchan Ko

Robust training and rigorous error analysis of physics-informed neural networks for the $p$-Laplace equation

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 analyses of PINNs remain confined to linear equations, and a substantial gap persists between PINNs and classical numerical analysis for nonlin...