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Many numerical algorithms have been established to reconstruct pressurefields from measured kinematic data with noise by Particle Image Velocimetry(PIV), such as the Pressure Poisson solver and the Omni-Directional Integration(ODI) method. This study adopts Gaussian Process Regression (GPR), aprobabilistic framework with an intrinsic de-noising mechanism to tackledrawbacks of traditional Pressure Poisson solver and compares the performancewith ODI. To evaluate the accuracy of the algorithm, GPR and ODI are tested indetail in a canonical setup of forced homogeneous isotropic turbulence from theJohns Hopkins Turbulence Database. According to the result, GPR has the samelevel of accuracy as ODI with optimized hyper-parameters for the isotropicturbulence flow. However, GPR has the tendency to flatten impulsive signals.Therefore, without further modifications, it is not suitable to detect flowstructures with impulsive true signals. The error propagation of the proposedframework is also analyzed and discussed in both physical and spectral spaces.

Pressure Reconstruction from the Measured Pressure Gradient Using Gaussian Process Regression