  In this paper, we show a physics-informed neural network solver for the
time-dependent surface PDEs. Unlike the traditional numerical solver, no
extension of PDE and mesh on the surface is needed. We show a simplified prior
estimate of the surface differential operators so that PINN's loss value will
be an indicator of the residue of the surface PDEs. Numerical experiments
verify efficacy of our algorithm.
