Revealing essential dynamics from high-dimensional fluid flow data and operators
Fluid Dynamics
2019-03-06 v1
Abstract
We consider concepts centered around modal analysis, data science, network science, and machine learning to reveal the essential dynamics from high-dimensional fluid flow data and operators. The presentation of the material herein is example-based and follows the author's keynote talk at the 32nd Computational Fluid Dynamics Symposium (Japan Society of Fluid Mechanics, Tokyo, December 11-13, 2018). This talk was delivered as a compilation of some of the research activities undertaken by the author's research group.
Cite
@article{arxiv.1903.01913,
title = {Revealing essential dynamics from high-dimensional fluid flow data and operators},
author = {Kunihiko Taira},
journal= {arXiv preprint arXiv:1903.01913},
year = {2019}
}
Comments
10 pages, 8 figures