BibTex format
@inproceedings{Kelshaw:2022,
author = {Kelshaw, D and Rigas, G and Magri, L},
title = {Physics-informed CNNs for super-resolution of sparse observations on dynamical systems},
url = {http://arxiv.org/abs/2210.17319v2},
year = {2022}
}
Publications
Citation
RIS format (EndNote, RefMan)
TY - CPAPER
AB - In the absence of high-resolution samples, super-resolution of sparseobservations on dynamical systems is a challenging problem with wide-reachingapplications in experimental settings. We showcase the application ofphysics-informed convolutional neural networks for super-resolution of sparseobservations on grids. Results are shown for the chaotic-turbulent Kolmogorovflow, demonstrating the potential of this method for resolving finer scales ofturbulence when compared with classic interpolation methods, and thuseffectively reconstructing missing physics.
AU - Kelshaw,D
AU - Rigas,G
AU - Magri,L
PY - 2022///
TI - Physics-informed CNNs for super-resolution of sparse observations on dynamical systems
UR - http://arxiv.org/abs/2210.17319v2
UR - http://hdl.handle.net/10044/1/101242
ER -