Imperial College London
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DrFangxinFang

Faculty of Engineering, Department of Earth Science & Engineering

Senior Research Fellow
 
 
 
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Contact

 

+44 (0)20 7594 1912f.fang

 
 
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Location

 

4.90Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Cheng:2020:10.1016/j.cma.2020.113375,
author = {Cheng, M and Fang, F and Pain, CC and Navon, IM},
doi = {10.1016/j.cma.2020.113375},
journal = {Computer Methods in Applied Mechanics and Engineering},
pages = {1--19},
title = {An advanced hybrid deep adversarial autoencoder for parameterized nonlinear fluid flow modelling},
url = {http://dx.doi.org/10.1016/j.cma.2020.113375},
volume = {372},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Considering the high computation cost required in conventional computation fluid dynamic simulations, machine learning methods have been introduced to flow dynamic simulations in years, aiming on reducing CPU time. In this work, we propose a hybrid deep adversarial autoencoder (VAE-GAN) to integrate generative adversarial network (GAN) and variational autoencoder (VAE) for predicting parameterized nonlinear fluid flows in spatial and temporal dimensions. High-dimensional inputs are compressed into the low-dimensional representations by nonlinear functions in a convolutional encoder. In this way, the predictive fluid flows reconstructed in a convolutional decoder contain the dynamic fluid flow physics of high nonlinearity and chaotic nature. In addition, the low-dimensional representations are applied to the adversarial network for model training and parameter optimization, which enables fast computation process. The capability of the hybrid VAE-GAN is illustrated by varying inputs on a flow past a cylinder test case as well as a second case of water column collapse. Numerical results show that this hybrid VAE-GAN has successfully captured the spatio-temporal flow features with CPU speed-up of three orders of magnitude. These promising results suggest that the hybrid VAE-GAN can play a critical role in efficiently and accurately predicting complex flows in future research efforts.
AU  - Cheng,M
AU  - Fang,F
AU  - Pain,CC
AU  - Navon,IM
DO  - 10.1016/j.cma.2020.113375
EP  - 19
PY  - 2020///
SN  - 0045-7825
SP  - 1
TI  - An advanced hybrid deep adversarial autoencoder for parameterized nonlinear fluid flow modelling
T2  - Computer Methods in Applied Mechanics and Engineering
UR  - http://dx.doi.org/10.1016/j.cma.2020.113375
UR  - https://www.sciencedirect.com/science/article/pii/S0045782520305600?via%3Dihub
VL  - 372
ER  -
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