Physics-informed Deep Super-resolution for Spatiotemporal Data

Understand

High-fidelity simulation of complex physical systems is exorbitantly expensive and inaccessible across spatiotemporal scales.

• Recently, there has been an increasing interest in leveraging deep learning to augment scientific data based on the coarse-grained simulations, which is of cheap computational expense and retains satisfactory solution accuracy.
• However, the major existing work focuses on data-driven approaches which rely on rich training datasets and lack sufficient physical constraints.
• To this end, we propose a novel and efficient spatiotemporal super-resolution framework via physics-informed learning, inspired by the independence between temporal and spatial derivatives in partial differential equations (PDEs).