Minseok Choi

The work proposes causality-enforced evolutional networks (CEEN), which divide the time domain into nonoverlapping subintervals, assign a unique neural network to each subinterval, build the loss from the integral form of the PDE (trapezoidal rule), and train sequentially from the initial time step, achieving higher accuracy than the original PINN on long-time integration of the KdV, Schrödinger, convection, and Allen-Cahn equations while requiring less computational cost and memory, with a parallelization algorithm for further speedup.

The work proposes causality-enforced evolutional networks (CEEN), which divide the time domain into nonoverlapping subintervals, assign a unique neural network to each subinterval, build the loss from the integral form of the PDE (trapezoidal rule), and train sequentially from the initial time step, achieving higher accuracy than the original PINN on long-time integration of the KdV, Schrödinger, convection, and Allen-Cahn equations while requiring less computational cost and memory, with a parallelization algorithm for further speedup.