Emerging trends in machine learning for computational fluid dynamics

TL;DR

This paper reviews emerging machine learning trends that enhance computational fluid dynamics, highlighting current benefits, future opportunities, and advocating for cautious optimism in integrating ML with CFD.

Contribution

It provides a comprehensive overview of recent ML advancements in CFD and discusses potential future developments and challenges.

Findings

ML has already improved CFD simulations in specific applications.

Emerging ML techniques show promise for future CFD advancements.

A balanced, cautious approach is recommended for adopting ML in CFD.

Abstract

The renewed interest from the scientific community in machine learning (ML) is opening many new areas of research. Here we focus on how novel trends in ML are providing opportunities to improve the field of computational fluid dynamics (CFD). In particular, we discuss synergies between ML and CFD that have already shown benefits, and we also assess areas that are under development and may produce important benefits in the coming years. We believe that it is also important to emphasize a balanced perspective of cautious optimism for these emerging approaches