Seungchan Ko

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

A robust training framework for PINNs solving nonlinear partial differential equations, together with a rigorous error analysis, is proposed and extended to a parametric setting in which the exponent, the source term, and the boundary condition may all vary with the parameters.