The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. It outlines fundamental machine learning methodologies and discusses their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation. Machine learning provides a powerful information processing framework that can enrich, and possibly even transform, current lines of fluid mechanics research and industrial applications.

中文翻译：

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流体力学机器学习

在来自多个时空尺度的现场测量、实验和大规模模拟的前所未有的大量数据的推动下，流体力学领域正在迅速发展。机器学习提供了丰富的技术来从数据中提取信息，这些信息可以转化为有关潜在流体力学的知识。此外，机器学习算法可以增强领域知识并自动执行与流量控制和优化相关的任务。本文概述了流体力学机器学习的过去历史、当前发展和新兴机会。它概述了基本的机器学习方法，并讨论了它们在理解、建模、优化和控制流体流动方面的用途。这些方法的优势和局限性是从科学探究的角度来解决的，科学探究将数据视为建模、实验和模拟的固有部分。机器学习提供了一个强大的信息处理框架，可以丰富甚至可能改变当前的流体力学研究和工业应用。