  The present work investigates the use of physics-informed neural networks
(PINNs) for the 3D reconstruction of unsteady gravity currents from limited
data. In the PINN context, the flow fields are reconstructed by training a
neural network whose objective function penalizes the mismatch between the
network predictions and the observed data and embeds the underlying equations
using automatic differentiation. This study relies on a high-fidelity numerical
experiment of the canonical lock-exchange configuration. This allows us to
benchmark quantitatively the PINNs reconstruction capabilities on several
training databases that mimic state-of-the-art experimental measurement
techniques for density and velocity. Notably, spatially averaged density
measurements by light attenuation technique (LAT) are employed for the training
procedure. An optimal experimental setup for flow reconstruction by PINNs is
proposed according to two criteria : the implementation complexity and the
accuracy of the inferred fields.
