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PhySR: Physics-informed deep super-resolution for spatiotemporal data
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DOI：10.1016/j.jcp.2023.112438.png)
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摘要
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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 (i.e., PhySR), 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, which outperforms existing baseline algorithms, based on extensive numerical experiments.(c) 2023 Elsevier Inc. All rights reserved.
Keyword:
Partial differential equations
Scientific data
Super-resolution
Physics-informed learning
Hard-encoding scheme
期刊
IF:
3.8
论文数:
1.6W
被引数:
7.4W
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