智能系统学报2026，Vol.21Issue(1)：2-18,17.DOI:10.11992/tis.202501017
深度学习方法在流场重建中的应用综述
Overview of the application of deep learning methods in flow field reconstruction
摘要
Abstract
High resolution flow field data has the characteristics of nonlinearity and large data volume,which makes it difficult to obtain through both experimental and simulation methods.Flow field reconstruction technology can fully utilize the observable information of the flow field to mine unobservable information,and recover high-resolution flow field data from sparse or low resolution flow field data.Deep learning methods have been widely applied in fluid mech-anics problems due to their powerful feature extraction and nonlinear fitting capabilities.Among them,flow field recon-struction methods based on deep learning have high research potential.This article investigates deep learning based flow field reconstruction methods and categorizes modeling approaches for flow field reconstruction problems from different perspectives.This paper provides a detailed summary of the research progress and achievements in flow field recon-struction methods for modal recombination,local global prediction,and element solver,and discusses the advantages and disadvantages of each method.Finally,the challenges faced by deep learning based flow field reconstruction tech-nology were summarized and analyzed,and future research directions were discussed.关键词
流场重建/深度学习/神经网络/计算流体力学/数值模拟/模态分解/超分辨率/数据增强Key words
flow field reconstruction/deep learning/neural networks/computational fluid dynamics/numerical simula-tion/mode decomposition/super-resolution/data augmentation分类
信息技术与安全科学引用本文复制引用
邵绪强,栗明宇,韩浩,王磊,王德生,王泠沄..深度学习方法在流场重建中的应用综述[J].智能系统学报,2026,21(1):2-18,17.基金项目
国家重点研发计划项目(2021YFF0604000). （2021YFF0604000）
