Artificial intelligence and machine learning in aerodynamics
Jiaqing Kou, Tianbai Xiao
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.