Physics-informed neural networks for gravity currents reconstruction from limited data. (arXiv:2211.09715v1 [physics.flu-dyn])

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 …

arxiv data gravity networks neural networks physics