#### Approach two: Deep PIV

**Introduction**

A fluid motion estimation algorithm based on deep neural networks is proposed. With the development of deep learning, it is possible to solve the problem of fluid image velocimetry by using convolutional neural network (CNN). The deep learning technology is innovatively applied to the PIV experiment. Specifically, two PIV neural networks are proposed based on FlowNetS and LiteFlowNet, respectively, which are used for optical flow estimation. The input of the networks is a particle image pair and the output is a global velocity field. In addition, a PIV data set is artificially generated for CNN training, which takes into account the physical properties and the image noise. The proposed CNN models are verified by a number of assessments and in real PIV experiments such as turbulent boundary layer. Without loss of precision, the computational efficiency is greatly improved compared with the variational optical flow method. This advantage provides possibility for real-time flow measurement and control.

**Paper**

1. S. Cai, S. Zhou, C. Xu, Q. Gao. Dense motion estimation of particle images via a convolutional neural network. Experiments in Fluids, 60: 73, 2019.

2. S. Cai, J. Liang, Q. Gao, C. Xu, R. Wei. Particle Image Velocimetry Based on a Deep Learning Motion Estimator, IEEE Transactions on Instrumentation and Measurement, PP(99):1-1, 2019.

3. S. Cai, J. Liang, S. Zhou, et al. Deep-PIV: a new framework of PIV using deep learning techniques, International Symposium on Particle Image Velocimetry. Munich, Germany, 2019.

**Patent**

1. C. Xu, S. Cai, Q. Gao, S.Zhou, One particle image velocimetry method based on convolutional neural network. Patent. Public.