Imperial College London
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ProfessorChristopherPain

Faculty of Engineering, Department of Earth Science & Engineering

Professorial Research Fellow
 
 
 
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Contact

 

+44 (0)20 7594 9322c.pain

 
 
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Location

 

4.96Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Cheng:2020:10.1016/j.cma.2020.113000,
author = {Cheng, M and Fang, F and Pain, CC and Navon, IM},
doi = {10.1016/j.cma.2020.113000},
journal = {Computer Methods in Applied Mechanics and Engineering},
pages = {1--18},
title = {Data -driven modelling of nonlinear spatio-temporal fluid flows using a deep convolutional generative adversarial network},
url = {http://dx.doi.org/10.1016/j.cma.2020.113000},
volume = {365},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Deep learning techniques for fluid flow modelling have gained significant attention in recent years. Advanced deep learning techniques achieve great progress in rapidly predicting fluid flows without prior knowledge of the underlying physical relationships. However, most of existing researches focused mainly on either sequence learning or spatial learning, rarely on both spatial and temporal dynamics of fluid flows (Reichstein et al., 2019). In this work, an Artificial Intelligence (AI) fluid model based on a general deep convolutional generative adversarial network (DCGAN) has been developed for predicting spatio-temporal flow distributions. In deep convolutional networks, the high-dimensional flows can be converted into the low-dimensional “latent” representations. The complex features of flow dynamics can be captured by the adversarial networks. The above DCGAN fluid model enables us to provide reasonable predictive accuracy of flow fields while maintaining a high computational efficiency. The performance of the DCGAN is illustrated for two test cases of Hokkaido tsunami with different incoming waves along the coastal line. It is demonstrated that the results from the DCGAN are comparable with those from the original high fidelity model (Fluidity). The spatio-temporal flow features have been represented as the flow evolves, especially, the wave phases and flow peaks can be captured accurately. In addition, the results illustrate that the online CPU cost is reduced by five orders of magnitude compared to the original high fidelity model simulations. The promising results show that the DCGAN can provide rapid and reliable spatio-temporal prediction for nonlinear fluid flows.
AU  - Cheng,M
AU  - Fang,F
AU  - Pain,CC
AU  - Navon,IM
DO  - 10.1016/j.cma.2020.113000
EP  - 18
PY  - 2020///
SN  - 0045-7825
SP  - 1
TI  - Data -driven modelling of nonlinear spatio-temporal fluid flows using a deep convolutional generative adversarial network
T2  - Computer Methods in Applied Mechanics and Engineering
UR  - http://dx.doi.org/10.1016/j.cma.2020.113000
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000539616200007&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://www.sciencedirect.com/science/article/pii/S0045782520301845?via%3Dihub
VL  - 365
ER  -
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