叶舒然1,2, 张珍1,2, 王一伟1,2, 黄晨光1,2
1. 中国科学院 力学研究所 流固耦合系统力学重点实验室, 北京 100190;
2. 中国科学院大学 工程科学学院, 北京 100049
YE Shuran1,2, ZHANG Zhen1,2, WANG Yiwei1,2, HUANG Chenguang1,2
1. Key Laboratory for Mechanics in Fluid Solid Coupling Systems, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China;
2. School of Engineering Science, University of Chinese Academy of Sciences, Beijing 100049, China
Abstract: With excellent performance, the deep learning architecture has enabled new developments in application of machine learning in fluid mechanics, and can cope with many challenges and needs in fluid mechanics. Due to powerful nonlinear mapping capabilities and hierarchical extraction of information features, the Convolutional Neural Network (CNN) has become a tool that cannot be ignored in current research on flow features. This paper summarizes the progress and achievements in this research area. First, the developments of deep learning for fluid mechanics and CNNs are briefly reviewed. Then, the research progress of using deep CNN in flow prediction, flow shape optimization, improving the accuracy of flow field visualization, and generation confrontation is introduced. Finally, prospects of application of deep learning in flow field recognition are discussed to provide a reference for subsequent research.