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基于卷积神经网络的深度学习流场特征识别及应用进展

发布日期: 2021-06-10 | 浏览次数: 382
基于卷积神经网络的深度学习流场特征识别及应用进展

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


Progress in deep convolutional neural network based flow field recognition and its applications

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

                                                    

摘要: 深度学习架构的出色性能使得机器学习在流体力学中的应用得到新的发展,可以应对流体力学中诸多问题和需求。卷积神经网络(CNN)强大的非线性映射能力以及分层提取信息特征的功能,使其成为当下流场特征研究不容忽视的工具。围绕这一研究前沿与热点问题,概述和归纳了这一研究领域的进展与成果。首先,对深度学习在流体力学中的发展以及卷积神经网络进行了简单的回顾。然后,从卷积神经网络能够识别特征出发,先后介绍了基于卷积的深度学习特征识别在流场预测、流动外形优化、流场可视化精度提升和生成对抗等应用方面的研究进展。最后,对深度学习在流场识别领域的应用进行了展望,为后续的研究提供参考。

关键词: 卷积神经网络, 流场识别, 流动预测, 外形优化, 泊松方程, 生成对抗网络, 深度学习

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.

Key words: convolutional neural network, flow field recognition, flow prediction, shape optimization, Poisson equation, generative adversarial network, deep learning

                                                    


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