Physics-Informed Convolutional Neural Networks for Corruption Removal on Dynamical Systems

• URL: http://arxiv.org/abs/2210.16215v1
• Date: Fri, 28 Oct 2022 15:43:55 GMT
• Title: Physics-Informed Convolutional Neural Networks for Corruption Removal on Dynamical Systems
• Authors: Daniel Kelshaw, Luca Magri
• Abstract summary: We propose physics-informed convolutional neural networks for stationary corruption removal. We showcase the methodology for 2D incompressible Navier-Stokes equations in the chaotic-turbulent flow regime.
• Score: 6.85316573653194
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Measurements on dynamical systems, experimental or otherwise, are often subjected to inaccuracies capable of introducing corruption; removal of which is a problem of fundamental importance in the physical sciences. In this work we propose physics-informed convolutional neural networks for stationary corruption removal, providing the means to extract physical solutions from data, given access to partial ground-truth observations at collocation points. We showcase the methodology for 2D incompressible Navier-Stokes equations in the chaotic-turbulent flow regime, demonstrating robustness to modality and magnitude of corruption.

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