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.