Prediction of Computational Fluid Dynamics Results using Convolutional LSTM

Abstract

In this paper, we propose a method for predicting computational fluid dynamics (CFD) results using Convolution LSTM. Convolutional LSTM is a method that combines Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). In addition, this method can predict future states with high accuracy by holding spatial information and time series. First, Convolution LSTM was trained using the visualization results of CFD analysis (image information). And it showed its usefulness. Next, we performed learning using physical quantities on this learning machine and obtained some prediction accuracy.