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

Daniel Kelshaw; Luca Magri

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arXiv · 2210.16215

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

Daniel Kelshaw·Luca Magri

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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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physics.flu-dyn cs.LG

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Daniel Kelshaw, Luca Magri. 2022-10-28. Physics-Informed Convolutional Neural Networks for Corruption Removal on Dynamical Systems. https://arxiv.org/abs/2210.16215

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