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Review | Open Access

Deep learning approaches in flow visualization

Can Liu1, Ruike Jiang1, Datong Wei1, Changhe Yang1, Yanda Li1, Fang Wang2, Xiaoru Yuan1,3 ( )
Key Laboratory of Machine Perception (Ministry of Education), and School of AI, Peking University, Beijing, China
China Aerodynamics Research and Development Center, Mianyang, China
National Engineering Laboratory for Big Data Analysis and Application, Peking University, Beijing, China
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Abstract

With the development of deep learning (DL) techniques, many tasks in flow visualization that used to rely on complex analysis algorithms now can be replaced by DL methods. We reviewed the approaches to deep learning technology in flow visualization and discussed the technical benefits of these approaches. We also analyzed the prospects of the development of flow visualization with the help of deep learning.

Keywords

Deep learningFlow visualizationData managementFeature extractionParticle tracing

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Advances in Aerodynamics
Volume 4 Issue 1,
December 2022
Pages 17-17
DOI: 10.1186/s42774-022-00113-1

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Cite this article:
Liu C, Jiang R, Wei D, et al. Deep learning approaches in flow visualization. Advances in Aerodynamics, 2022, 4(1): 17. https://doi.org/10.1186/s42774-022-00113-1

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Received: 11 October 2021
Accepted: 22 March 2022
Published: 14 April 2022
© The Author(s) 2022.

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