Flow reconstruction with uncertainty quantification from noisy measurements based on Bayesian physics-informed neural networks
Hailong Liu(Sun Yat-sen University), Shengze Cai(ZheJiang Institute For Food and Drug Control), Rui Deng(Sun Yat-sen University), Shipeng Wang(Merchants Chongqing Communications Research and Design Institute), Xuhui Meng(John Brown University), Chao Xu(Zhejiang University of Technology), Zhi Hu Wang(State Key Laboratory of Industrial Control Technology)
Cited by 14
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