Physics-Informed CNNs for Super-Resolution of Sparse Observations on Dynamical Systems
Fluid Dynamics
2022-11-08 v2 Machine Learning
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.
Cite
@article{arxiv.2210.17319,
title = {Physics-Informed CNNs for Super-Resolution of Sparse Observations on Dynamical Systems},
author = {Daniel Kelshaw and Georgios Rigas and Luca Magri},
journal= {arXiv preprint arXiv:2210.17319},
year = {2022}
}
Comments
Published in NeurIPS 2022: Machine Learning and the Physical Sciences Workshop. Code at https://github.com/magrilab/pisr. arXiv admin note: text overlap with arXiv:2210.16215