Feature tracking in physics-informed neural networks via joint optimization of nonlinear deformation manifolds: application to shocks

Physics-informed neural networks (PINNs) often converge to inaccurate solutions for conservation laws with shocks, because uniformly distributed collocation points undersample localized features and let the residual be dominated by regions that are already well resolved. We propose a feature-trackin