Deep learning for fluid velocity field estimation: A review

Deep learning technique, has made tremendous progress in fluid mechanics in recent years, because of its mighty feature extraction capacity from complicated and massive fluid data. Motion estimation and analysis of fluid data is one of the significant research topics in fluid mechanics. In this paper, we provide a comprehensive review of fluid motion (i.e., velocity field) estimation methods based on deep learning. Essentially, the fluid super-resolution (SR) reconstruction task can also be regarded as an velocity field estimation from low resolution to high resolution. To this end, we mainly give a review on two topics: fluid motion estimation and later velocity field super-resolution reconstruction. Specifically, we first introduce the basic principle and component of deep learning methods. We then review and analyze deep learning based methods on fluid motion estimation. Note we mainly investigate the commonly used fluid motion estimation approach here, particle image velocimetry (PIV) algorithm, which extract velocity field from successive particle images pair in a non-contact manner. In addition, SR reconstruction methods for velocity fields based on deep learning technique are also reviewed. Eventually, we give a discussion and possible routes for the future research works. To our knowledge, this paper are the first to give a review of deep learning-based approaches for fluid velocity field estimation.

Automated summary

Deep learning technique has made significant progress in fluid mechanics by extracting powerful features from complex and massive fluid data. This paper provides a comprehensive review of deep learning-based methods for fluid motion estimation and velocity field super-resolution reconstruction. The commonly used fluid motion estimation approach, particle image velocimetry (PIV) algorithm, is investigated here. Additionally, the paper discusses future research directions.