论文标题
序列序列AE-CONVLSTM网络，用于建模PDE系统的动力学
Sequence to sequence AE-ConvLSTM network for modelling the dynamics of PDE systems
论文作者
论文摘要
本文详细介绍了卷积LSTM（Convlstm）网络，并介绍了改进的Convlstm网络的自动编码器，称为AE-ConvlSTM。 AE-CONVLSTM也是序列网络的序列，可以通过将隐藏状态从一个编码器传递到另一个编码，可以预测动态系统的长时间演变。当使用数据训练时，并且在另一种情况下，使用管理方程（无数据），即物理学约束时，该网络在预测不稳定2-D粘性汉堡的动态演化方面表现良好。此外，使用AE-ConvlSTM来预测两个不稳定的Navier-Stokes问题的时间演变。这些问题的压力和速度场的幅度级级不同，并且这些场以不同的速率随着时间的流逝而演变。据观察，可以使用数据对网络进行训练，但是在使用管理方程式受到物理限制的训练时，AE-ConvlSTM无法训练时间演变。
The article explains the convolutional LSTM (ConvLSTM) network in detail and introduces an improved auto-encoder version of the ConvLSTM network called AE-ConvLSTM. AE-ConvLSTM is also a sequence to sequence network that can predict long time evolution of a dynamical system by passing hidden states from one encoder to another. The network performed well in predicting the dynamic evolution of unsteady 2-D viscous Burgers when trained using data and, in another case, using governing equation (without data), i.e., physics-constrained. Further, AE-ConvLSTM was used in an effort to predict the time evolution of two unsteady Navier-Stokes problems. These problems have coupled pressure and velocity field having different magnitude order, and these fields evolve in time at a different rate. It was observed that the network could be trained using data, but while training using physics-constrained via governing equations, AE-ConvLSTM fails to train for time evolution.
