Enhancing Computational Fluid Dynamics with Machine Learning

TL;DR

This paper discusses how machine learning can significantly improve computational fluid dynamics by accelerating simulations, enhancing turbulence models, and developing better reduced-order models, while also addressing emerging opportunities and limitations.

Contribution

It highlights new machine learning applications in CFD, including acceleration, turbulence modeling, and reduced-order models, with insights into future potential and challenges.

Findings

ML accelerates CFD simulations

Improves turbulence closure models

Proposes advanced reduced-order models

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.