%A WANG Yixing, HAN Renkun, LIU Ziyang, ZHANG Yang, CHEN Gang %T Progress of deep learning modeling technology for fluid mechanics %0 Journal Article %D 2021 %J Acta Aeronautica et Astronautica Sinica %R 10.7527/S1000-6893.2020.24779 %P 524779-524779 %V 42 %N 4 %U {https://hkxb.buaa.edu.cn/CN/abstract/article_18259.shtml} %8 %X Deep learning technology has brought subversive changes in many fields, such as image processing, language translation, disease diagnosis, and game competition. Due to the characteristics of high dimensionality, strong nonlinearity and large amount of data, fluid mechanics is an important area where deep learning is good at and could bring out innovation in research paradigm. At present, the deep learning technology has been initially applied in the field of fluid mechanics, and its application potential has been gradually confirmed. Based on the deep learning technology for fluid mechanics and the recent research results of our group, this paper discusses the deep learning modeling technology for fluid mechanics and its latest progress. First, the basic theory of the deep learning technology is introduced, and the mathematics behind the deep learning methods commonly used in fluid mechanics modeling are explained. Then, the progress of the deep learning technology involved in several typical application scenarios of artificial intelligence of fluid mechanics, such as basic control equation, flow field reconstruction, and feature modeling and application, are introduced. Finally, the challenges and future development trend of the deep learning modeling technology of fluid mechanics are discussed.