Mitigating Numerical Stiffness in Least-Squares Formulations of Elliptic PDEs for Physics-Informed Neural Networks

Phil-Alexander Hofmann, Michael Hecht

We present theoretical insights into $H^{-1}$ residual loss formulations of physics-informed neural networks (PINNs) for learning solutions of partial differential equations (PDEs)…