Sequence to sequence AE-ConvLSTM network for modelling the dynamics of PDE systems
Abstract
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.
Cite
@article{arxiv.2208.07315,
title = {Sequence to sequence AE-ConvLSTM network for modelling the dynamics of PDE systems},
author = {Priyesh Rajesh Kakka},
journal= {arXiv preprint arXiv:2208.07315},
year = {2022}
}