Abstract

Sparsely available data points cause a numerical error on finite differences which hinder to modeling the dynamics of physical systems. The discretization error becomes even larger when the sparse data are irregularly distributed so that the data defined on an unstructured grid. It is hard to build deep learning models to handle physics-governing observations on the unstructured grid. In this paper, we propose a novel architecture named Physics-aware Difference Graph Networks (PA-DGN) that exploit neighboring information to learn finite differences inspired by physics equations. PA-DGN further leverages data-driven end-to-end learning to discover underlying dynamical relations between the spatial and temporal differences in given observations. We demonstrate the superiority of PA-DGN in the approximation of directional derivatives and the prediction of graph signals on the synthetic data and the real-world climate observations from land-based weather stations.