  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.
