Physics Informed Neural Networks (PINNs)for approximating nonlinear dispersive PDEs

We propose a novel algorithm, based on physics-informed neural networks (PINNs) to efficiently approximate solutions of nonlinear dispersive PDEs such as the KdV-Kawahara, Camassa-Holm andBenjamin-Ono equations. The stability of solutions of these dispersive PDEs is leveraged to prove rigorous bounds on the resulting error. We present several numerical experiments to demonstrate thatPINNs can approximate solutions of these dispersive PDEs very accurately