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Journal of Visualization

, Volume 22, Issue 1, pp 95–108 | Cite as

A novel in situ compression method for CFD data based on generative adversarial network

  • Yang LiuEmail author
  • Yueqing Wang
  • Liang Deng
  • Fang Wang
  • Fang Liu
  • Yutong Lu
  • Sikun Li
Regular Paper
  • 100 Downloads

Abstract

As one of the main technologies of in situ visualization, data compression plays a key role in solving I/O bottleneck and has been intensively studied. However, existing methods take too much compression time to meet the requirement of in situ processing on computational fluid dynamics (CFD) flow field data. To address this problem, we introduce deep learning into CFD data compression and propose a novel in situ compression method based on generative adversarial network (GAN) in this paper. In specific, the proposed method samples small patches from CFD data and trains a GAN which includes two convolutional neural networks: the discriminative network and the generative network. The discriminative network is responsible for compressing data on compute nodes, while the generative network is used to reconstruct data on visualization nodes. Compared with the existing CFD data compression methods, our method has great advantages in compression time and manages to adjust compression ratio according to acceptable reconstruction effect, showing its applicability for loosely coupled in situ visualization. Extensive experimental results demonstrate the good generalization of the proposed method on many datasets.

Graphical Abstract

Keywords

In situ visualization Data compression CFD flow field GAN 

Notes

Acknowledgements

The authors wish to thank Dr. Dong Sun for his guidance in CFD. This work was supported in part by the National Key Research and Development Program of China (#2016YFB0200701, #2018YFB0203904 and #2016YFB1000302), National Nature Science Foundation of China (#U1611261) and the Program for Guangdong Introducing Innovative and Enterpreneurial Teams (#2016ZT06D211).

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Copyright information

© The Visualization Society of Japan 2018

Authors and Affiliations

  1. 1.College of Computer at National University of Defense TechnologyChangshaChina
  2. 2.Computational Aerodynamics Institute at China Aerodynamics Research and Development CenterMianyangChina
  3. 3.School of Data and Computer Science at Sun Yat-Sen UniversityGuangzhouChina

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