The transformative potential of machine learning for experiments in fluid mechanics

Abstract

The field of machine learning (ML) 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 article highlights several aspects of experimental fluid mechanics that stand to benefit from progress in ML, including augmenting the fidelity and quality of measurement techniques, improving experimental design and surrogate digital-twin models and 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. Recent advances in machine learning are enabling progress in several aspects of experimental fluid mechanics. This Perspective article focuses on augmenting the quality of measurement techniques, improving experimental design and enabling real-time estimation and control.