  While physics conveys knowledge of nature built from an interplay between
observations and theory, it has been considered less importantly in deep neural
networks. Especially, there are few works leveraging physics behaviors when the
knowledge is given less explicitly. In this work, we propose a novel
architecture called Differentiable Physics-informed Graph Networks (DPGN) to
incorporate implicit physics knowledge which is given from domain experts by
informing it in latent space. Using the concept of DPGN, we demonstrate that
climate prediction tasks are significantly improved. Besides the experiment
results, we validate the effectiveness of the proposed module and provide
further applications of DPGN, such as inductive learning and multistep
predictions.
