Physics-informed neural networks for high-resolution weather reconstruction from sparse weather stations.
Álvaro Moreno SotoAlejandro CervantesManuel SolerPublished in: Open research Europe (2024)
The effect of time and spatial resolution over the capability of the PINN to accurately reconstruct fluid phenomena is thoroughly discussed through a parametric study, concluding that a proper tuning of the neural network's loss function during training is of utmost importance.