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

Numerical Analysis 2022-03-01 v1 Numerical Analysis

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.

Keywords

Cite

@article{arxiv.2202.13429,
  title  = {DeepPropNet -- A Recursive Deep Propagator Neural Network for Learning Evolution PDE Operators},
  author = {Lizuo Liu and Wei Cai},
  journal= {arXiv preprint arXiv:2202.13429},
  year   = {2022}
}
R2 v1 2026-06-24T09:55:31.072Z