Enhancing Computational Fluid Dynamics with Machine Learning
Fluid Dynamics
2022-07-04 v2 Machine Learning
Computational Physics
Abstract
Machine learning is rapidly becoming a core technology for scientific computing, with numerous opportunities to advance the field of computational fluid dynamics. In this Perspective, we highlight some of the areas of highest potential impact, including to accelerate direct numerical simulations, to improve turbulence closure modeling, and to develop enhanced reduced-order models. We also discuss emerging areas of machine learning that are promising for computational fluid dynamics, as well as some potential limitations that should be taken into account.
Cite
@article{arxiv.2110.02085,
title = {Enhancing Computational Fluid Dynamics with Machine Learning},
author = {Ricardo Vinuesa and Steven L. Brunton},
journal= {arXiv preprint arXiv:2110.02085},
year = {2022}
}
Comments
15 pages, 4 figures