Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics
A physics-informed neural network framework that integrates the incompressible Navier-Stokes equations with velocity measurements coming from experimental flow field data is proposed that enhances the reconstruction of velocity fields, enables the estimation of unmeasured quantities such as pressure and wall shear stre...