Gravity currents in oceanic flows require simultaneous measurements of
pressure and velocity to assess energy flux, which is crucial for predicting
fluid circulation, mixing, and overall energy budget. In this paper, we apply
Physics Informed Neural Networks (PINNs) to infer velocity and pressure field
from Light Attenuation Technique (LAT) measurements for gravity current induced
by lock-exchange. In a PINN model, physical laws are embedded in the loss
function of a neural network, such that the model fits the training data but is
also constrained to reduce the residuals of the governing equations. PINNs are
able to solve ill-posed inverse problems training on sparse and noisy data, and
therefore can be applied to real engineering applications. The noise robustness
of PINNs and the model parameters are investigated in a 2 dimensions toy case
on a lock-exchange configuration , employing synthetic data. Then we train a
PINN with experimental LAT measurements and quantitatively compare the velocity
fields inferred to PIV measurements performed simultaneously on the same
experiment. Finally, we study the energy flux field J=pu
derived from the model. The results state that accurate and useful quantities
can be derived from a PINN model trained on real experimental data which is
encouraging for a better description of gravity currents and improve models of
ocean circulation