Enhancing Computational Fluid Dynamics with Machine Learning
This is a perspective paper that highlights opportunities for researchers in computational fluid dynamics, but it is incremental as it reviews existing ideas rather than introducing new findings.
The paper discusses the potential of machine learning to advance computational fluid dynamics by accelerating simulations, improving turbulence modeling, and enhancing reduced-order models, without presenting specific results or numbers.
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.