The field of machine learning has rapidly advanced the state of the art in many fields of science and engineering, including experimental fluid dynamics, which is one of the original big-data disciplines. This perspective will highlight several aspects of experimental fluid mechanics that stand to benefit from progress advances in machine learning, including: 1) augmenting the fidelity and quality of measurement techniques, 2) improving experimental design and surrogate digital-twin models and 3) enabling real-time estimation and control. In each case, we discuss recent success stories and ongoing challenges, along with caveats and limitations, and outline the potential for new avenues of ML-augmented and ML-enabled experimental fluid mechanics.

翻译：机器学习领域的快速发展使得很多科学和工程领域取得了重大进展，其中的一个原始的大数据学科，实验流体力学，也从中大受益。该论文主要讨论机器学习在实验流体力学领域中具有潜在可能的几个方面： 1）提高测量技术的保真度和质量，2）改善实验设计和代理数字孪生模型，以及3）实现实时估计和控制。在论文中，我们探讨了近期的成功案例和持续存在的挑战，以及可能有利于机器学习增强和实现的实验流体力学的新途径。