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：机器学习领域已迅速推动了许多科学与工程领域的技术进步，包括作为最早的大数据学科之一的实验流体动力学。本综述将重点阐述实验流体力学的若干方面如何受益于机器学习的进展，包括：1) 增强测量技术的保真度和质量；2) 改进实验设计与替代性数字孪生模型；3) 实现实时估计与控制。针对每个方面，我们讨论了近年来的成功案例与当前挑战，同时指出注意事项与局限性，并概述了机器学习增强和机器学习赋能的实验流体力学的新兴发展潜力。