  We present a structure preserving PINN for solving a series of time dependent
PDEs with periodic boundary. Our method can incorporate the periodic boundary
condition as the natural output of any deep neural net, hence significantly
improving the training accuracy of baseline PINN. Together with mini-batching
and other PINN variants (SA-PINN, RBA-PINN, etc.), our structure preserving
PINN can even handle stiff PDEs for modeling a wide range of
convection-diffusion and reaction-diffusion processes. We demonstrate the
effectiveness of our PINNs on various PDEs from Allen Cahn, Gray Scott to
nonlinear Schrodinger.
