  Volume-resolving imaging techniques are rapidly advancing progress in
experimental fluid mechanics. However, reconstructing the full and structured
Eulerian velocity and pressure fields from sparse and noisy particle tracks
obtained experimentally remains a significant challenge. We introduce a new
method for this reconstruction, based on Physics-Informed Neural Networks
(PINNs). The method uses a Neural Network regularized by the Navier-Stokes
equations to interpolate the velocity data and simultaneously determine the
pressure field. We compare this approach to the state-of-the-art Constrained
Cost Minimization method [1]. Using data from direct numerical simulations and
various types of synthetically generated particle tracks, we show that PINNs
are able to accurately reconstruct both velocity and pressure even in regions
with low particle density and small accelerations. PINNs are also robust
against increasing the distance between particles and the noise in the
measurements, when studied under synthetic and experimental conditions.
