PhySR: Physics-informed deep super-resolution for spatiotemporal data

Pu Ren, Chengping Rao, Yang Liu, Zihan Ma, Qi Wang, Jian-Xun Wang, Hao Sun

Abstract: Highlights•Proposed a novel physics-informed learning model for super-resolution of spatiotemporal data.•Developed a deep convolutional-recurrent neural network architecture.•Imposed boundary conditions in a forcible manner to improve reconstruction accuracy.•Demonstrated efficacy of the method by extensive numerical experiments.•Method outperforms existing state-of-the-art baseline algorithms.