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.

翻译：机器学习正在迅速成为科学计算的核心技术,有很多机会推进计算流体动态领域。 从这个角度看,我们强调一些具有最大潜在影响的领域,包括加快直接数字模拟、改进动荡闭合模型和开发强化减序模型。 我们还讨论了对计算流体动态有希望的机械学习新领域,以及应当考虑的一些潜在限制。