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包括实验流体动力学在内的多个科学与工程领域的前沿发展，而实验流体动力学本身是最早的大数据学科之一。本综述将重点阐述实验流体力学中多个有望从机器学习进步中获益的方面，包括：1) 提升测量技术的保真度与质量，2) 优化实验设计与替代数字孪生模型，以及3) 实现实时估计与控制。针对每个方面，我们将讨论近年来的成功案例、当前挑战、注意事项与局限性，并概述机器学习增强型与机器学习驱动型实验流体力学的新途径潜力。