[1]SHAO Xuqiang,LI Mingyu,HAN Hao,et al.Overview of the application of deep learning methods in flow field reconstruction[J].CAAI Transactions on Intelligent Systems,2026,21(1):2-18.[doi:10.11992/tis.202501017]
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Overview of the application of deep learning methods in flow field reconstruction

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 21 Number of periods: 2026 1 Page number: 2-18 Column: 综述 Public date: 2026-03-05
Title:
Overview of the application of deep learning methods in flow field reconstruction
Author(s):
SHAO Xuqiang1;  LI Mingyu1; 2;  HAN Hao2;  WANG Lei2;  WANG Desheng2;  WANG Lingyun2
1. School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China;
2. State Key Laboratory of NBC Protection of Civilian, Beijing 102205, China
Keywords:
flow field reconstruction; deep learning; neural networks; computational fluid dynamics; numerical simulation; mode decomposition; super-resolution; data augmentation
CLC:
TP391.9
DOI:
10.11992/tis.202501017
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 mechanics problems due to their powerful feature extraction and nonlinear fitting capabilities. Among them, flow field reconstruction 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 reconstruction 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 technology were summarized and analyzed, and future research directions were discussed.
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