Physics-Informed Convolutional Neural Networks for Corruption Removal on Dynamical Systems
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.
Cite
@article{arxiv.2210.16215,
title = {Physics-Informed Convolutional Neural Networks for Corruption Removal on Dynamical Systems},
author = {Daniel Kelshaw and Luca Magri},
journal= {arXiv preprint arXiv:2210.16215},
year = {2022}
}
Comments
Published in NeurIPS 2022: Machine Learning and the Physical Sciences Workshop. Code at http://github.com/magriLab/PICR