We propose a meshless method to compute pressure fields from image velocimetry data,
regardless of whether this is available on a regular grid as in cross-correlation based …

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 …

Experimentally-measured pressure fields play an important role in understanding many fluid
dynamics problems. Unfortunately, pressure fields are difficult to measure directly with non …

In this work, we present a method to perform 2D and 3D omnidirectional pressure integration
from velocity measurements with a single-iteration matrix inversion approach. This work …

An analytical framework for the propagation of velocity errors into PIV-based pressure
calculation is extended. Based on this framework, the optimal spatial resolution and the …

This work introduces a method to estimate the uncertainty of the pressure fields
reconstructed from particle image velocimetry/particle tracking velocimetry (PIV/PTV) …

We propose a simple boundary condition regularization strategy to reduce error propagation
in pressure field reconstruction from corrupted image velocimetry data. The core idea is to …

This text describes a method to simultaneously reconstruct flow states and determine
particle properties from Lagrangian particle tracking (LPT) data. LPT is a popular …

Reconstruction of the pressure field from experimental velocity data is challenging due to
measurement noise, and no single method performs well for all experimental conditions …

Recovering pressure fields from image velocimetry measurements has two general
strategies: i) directly integrating the pressure gradients from the momentum equation and ii) …