Ioannis G. Kevrekidis

Going with the flow to solve for symmetry-driven PDE dynamics with physics-informed neural networks

We address a computational framework that integrates symmetry reduction into Physics-Informed Neural Networks (PINNs) for analysing symmetry-driven dynamics in nonlinear partial differential equations (PDEs). Using an auxiliary network to learn time-dependent transformations, we...