Comparative assessment for pressure field reconstruction based on physics-informed neural network

In this paper, a physics-informed neural network (PINN) is used to determine pressure fields from the experimentally measured velocity data. As a novel method of data assimilation, PINN can simultaneously optimize velocity and solve pressure by embedding the Navier-Stokes equations into the loss function. The PINN method is compared with two traditional pressure reconstruction algorithms, i.e., spectral decomposition-based fast pressure integration and irrotation correction on pressure gradient and orthogonal-path integration, and its performance is numerically assessed using two kinds of flow motions, namely, Taylor's decaying vortices and forced isotropic turbulence. In the case of two-dimensional decaying vortices, critical parameters of PINN have been investigated with and without considering measurement errors. Regarding the forced isotropic turbulence, the influence of spatial resolution and out-of-plane motion on pressure reconstruction is assessed. Finally, in an experimental case of a synthetic jet impinging on a solid wall, the PINN is used to determine the pressure from the velocity fields obtained by the planar particle image velocimetry. All results show that the PINN-based pressure reconstruction is superior to other methods even if the velocity fields are significantly contaminated by the measurement errors.