Artificial intelligence and machine learning in aerodynamics

Jiaqing Kou; Tianbai Xiao; Jiaqing Kou; Tianbai Xiao

doi:10.3934/mina.2024009

Metascience in Aerospace

2024, Volume 1, Issue 2: 190-218. doi: 10.3934/mina.2024009

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Review Special Issues

Artificial intelligence and machine learning in aerodynamics

Jiaqing Kou ¹,
Tianbai Xiao ^{2,3
,
,}

1.
Department of Aerodynamics, School of Aeronautics, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Shaanxi, Xi'an, China
2.
State Key Laboratory of High Temperature Gas Dynamics and Centre for Interdisciplinary Research in Fluids, Institute of Mechanics, Chinese Academy of Sciences, Beijing, 100190, China
3.
School of Engineering Science, University of Chinese Academy of Sciences, Beijing, 100049, China

Received: 19 December 2023 Revised: 09 March 2024 Accepted: 14 March 2024 Published: 19 April 2024

With the increasing availability of flow data from simulation and experiment, artificial intelligence and machine learning are revolutionizing the research paradigm in aerodynamics and related disciplines. The integration of machine learning with theoretical, computational, and experimental investigations unlocks new possibilities for solving cutting-edge problems. In this paper, we review the status of artificial intelligence and machine learning in aerodynamics, including knowledge discovery, theoretical modeling, numerical simulation, and multidisciplinary applications. Representative techniques and successful applications are summarized. Finally, despite successful applications, challenges still remain, which are discussed in the conclusion.
- artificial intelligence,
- machine learning,
- deep learning,
- fluid mechanics,
- aerodynamics,
- multiphysics
Citation: Jiaqing Kou, Tianbai Xiao. Artificial intelligence and machine learning in aerodynamics[J]. Metascience in Aerospace, 2024, 1(2): 190-218. doi: 10.3934/mina.2024009

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Abstract

With the increasing availability of flow data from simulation and experiment, artificial intelligence and machine learning are revolutionizing the research paradigm in aerodynamics and related disciplines. The integration of machine learning with theoretical, computational, and experimental investigations unlocks new possibilities for solving cutting-edge problems. In this paper, we review the status of artificial intelligence and machine learning in aerodynamics, including knowledge discovery, theoretical modeling, numerical simulation, and multidisciplinary applications. Representative techniques and successful applications are summarized. Finally, despite successful applications, challenges still remain, which are discussed in the conclusion.

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