Recurrent graph optimal transport for learning 3D flow motion in particle tracking
Jiaming Liang(State Key Laboratory of Industrial Control Technology), Shengze Cai(ZheJiang Institute For Food and Drug Control), Chao Xu(Zhejiang University of Technology)
Cited by 36
Related Papers
Physics-informed neural networks (PINNs) for fluid mechanics: a review
|Acta Mechanica Sinica|2021|1.8k
NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations
|Journal of Computational Physics|2020|1.1k
Physics-Informed Neural Networks for Heat Transfer Problems
|Journal of Heat Transfer|2021|1.1k
Flow over an espresso cup: inferring 3-D velocity and pressure fields from tomographic background oriented Schlieren via physics-informed neural networks
|Journal of Fluid Mechanics|2021|333
Dense motion estimation of particle images via a convolutional neural network
|Experiments in Fluids|2019|206