Physics-informed Deep Super-resolution for Spatiotemporal Data

TL;DR

This paper introduces a physics-informed deep learning framework for spatiotemporal super-resolution of physical systems, combining PDE-based constraints with neural networks to enhance simulation accuracy efficiently.

Contribution

The proposed method uniquely integrates physics-informed learning with neural networks for super-resolution, leveraging PDE properties and boundary conditions for improved physical fidelity.

Findings

Outperforms baseline algorithms in accuracy and efficiency

Effectively incorporates physical constraints into deep learning models

Achieves high-quality super-resolution with reduced computational cost

Abstract

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). The general principle is to leverage the temporal interpolation for flow estimation, and then introduce convolutional-recurrent neural networks for learning temporal refinement. Furthermore, we employ the stacked residual blocks with wide activation and sub-pixel layers with pixelshuffle for spatial reconstruction, where feature extraction is conducted in a low-resolution latent space. Moreover, we consider hard imposition of boundary conditions in the network to improve reconstruction accuracy. Results demonstrate the superior effectiveness and efficiency of the proposed method compared with baseline algorithms through extensive numerical experiments.