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- Deep-learning-based image preprocessing for particle image velocimetry . . .
The purpose of this study is to propose a deep-learning-based technique for PIV image preprocessing Specifically, we first designed a deep convolutional network called Bilateral-CNN for a pair image preprocessing task, which embeds residual learning and batch normalization
- BICSNet: Deep Learning for PIV Data Correction
The network is trained to emulate real-world PIV scenarios using approximately 73,000 synthetic images representing a range of Mach numbers, deflection angles, shock strengths, particle properties, and magnifications
- Particle Image Velocimetry Based on a Deep Learning Motion Estimator
Abstract: Particle image velocimetry (PIV), as a common technology for analyzing the global flow motion from images, plays a significant role in experimental fluid mechanics In this article, we investigate the deep learning-based techniques for such a fluid motion estimation problem
- 基于深度神经网络的「端到端」学习位移场的方法，用于粒子 . . .
研究人员提出的 RAFT-PIV，是一种用于 PIV 应用中光流估计的深度神经网络架构。 RAFT-PIV 在公共 PIV 数据库上实现了最先进的准确性，并且优于现有的基于监督和无监督学习的方法。 迭代流更新使后续的流细化成为可能，这可能是 RAFT-PIV 最突出的方面。
- Particle Image Velocimetry Based on a Deep Learning Motion Estimator
Particle image velocimetry (PIV), as a common technology for analysing the global flow motion from images, plays a significant role in experimental fluid mechanics In this paper, we
- Particle image velocimetry based on a deep neural network
As an experimental technique for fluid mechanics,particle image velocimetry (PIV)can extract global and quantitative velocity field from images With the development of artificial intelligence,designing PIV method based on deep learning is quite promising and worth studying First,the authors in this paper introduce the optical flow neural
- Twins-PIVNet: Spatial attention-based deep learning framework for . . .
We introduce Twins-PIVNet, a deep learning framework for PIV optical flow estimation that leverages a spatial attention-based Vision Transformer archi-tecture Its self-attention mechanism captures multi-scale features of particle motion, significantly improving the dense flow field estimation
- Pyramidal deep-learning network for dense velocity field . . . - Springer
This paper reports an end-to-end convolutional neural network, namely PIV-PWCNet, to reconstruct the dense velocity field from particle image pairs The main aim is to improve the accuracy and robustness of the velocimetry algorithms, meanwhile maintain a low computational cost
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