论文标题
Deeppropnet-用于学习进化PDE操作员的递归深层传播器神经网络
DeepPropNet -- A Recursive Deep Propagator Neural Network for Learning Evolution PDE Operators
论文作者
论文摘要
在本文中，我们通过使用一个单个神经网络传播器递归地向Evolution Operation提出了一个深层神经网络近似，以使时间依赖于时间依赖的PDE系统，该系统以具有内置因果关系特征的Pod-Deeponet的形式，用于较小的时间间隔。显示了中等大小的训练有素的培养纸，可以在整个时间间隔内准确预测波解决方案。
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.
