Physics-Informed CNNs for Super-Resolution of Sparse Observations on Dynamical Systems

• URL: http://arxiv.org/abs/2210.17319v1
• Date: Mon, 31 Oct 2022 13:36:18 GMT
• Title: Physics-Informed CNNs for Super-Resolution of Sparse Observations on Dynamical Systems
• Authors: Daniel Kelshaw, Georgios Rigas, Luca Magri
• Abstract summary: We show the application of physics-informed convolutional neural networks for super-resolution of sparse observations on grids. Results are shown for the chaotic-turbulent Kolmogorov flow, demonstrating the potential of this method for resolving finer scales of turbulence.
• Score: 5.8010446129208155
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the absence of high-resolution samples, super-resolution of sparse observations on dynamical systems is a challenging problem with wide-reaching applications in experimental settings. We showcase the application of physics-informed convolutional neural networks for super-resolution of sparse observations on grids. Results are shown for the chaotic-turbulent Kolmogorov flow, demonstrating the potential of this method for resolving finer scales of turbulence when compared with classic interpolation methods, and thus effectively reconstructing missing physics.

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