The transformative potential of machine learning for experiments in fluid mechanics
It addresses the potential for ML to advance experimental fluid mechanics, an incremental review of existing applications.
This perspective paper examines how machine learning can enhance experimental fluid mechanics by improving measurement techniques, experimental design, and real-time control, highlighting recent successes and ongoing challenges in the field.
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.