流体力学深度学习建模技术研究进展

深度学习技术在图像处理、语言翻译、疾病诊断、游戏竞赛等领域已带来了颠覆性的变化。流体力学问题由于维度高、非线性强、数据量大等特点，恰恰是深度学习擅长并可以带来研究范式创新的重要领域。目前，深度学习技术已在流体力学领域得到了初步应用，其应用潜力逐渐得到证实。以流体力学深度学习技术为背景，结合课题组近期研究结果，探讨了流体力学深度学习建模技术及其最新进展。首先，对深度学习技术所涉及的基本理论做了介绍，阐释流场建模中常用深度学习方法背后的数学原理。其次，分别对流体力学控制方程、流场重构、特征量建模和应用等几个典型的人工智能与流体力学交叉问题应用场景所涉及的深度学习技术研究进展进行了介绍。最后，探讨了流体力学深度学习建模技术所面临的挑战与未来发展趋势。

Progress of deep learning modeling technology for fluid mechanics

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.