DeepPropNet -- A Recursive Deep Propagator Neural Network for Learning Evolution PDE Operators

Abstract: In this paper, we propose a deep neural network approximation to the evolution operator for time dependent PDE systems over long time period by recursively using one single neural network propagator, in the form of POD-DeepONet with built-in causality feature, for a small time interval. The trained DeepPropNet of moderate size is shown to give accurate prediction of wave solutions over the whole time interval.