Abstract
Recent advances in machine learning and an ever increasing availability of data offer new perspectives (and hope) for solving long standing fluid mechanics problems. Despite early connections dating back to Kolmogorov, the link between Fluid Mechanics and Machine Learning (ML) has not been fully explored. The situation is rapidly changing with ML algorithms entering in numerous efforts for modeling, optimising, and controlling fluid flows. In this talk I will present works from our group on the interface of Fluid Mechanics and ML ranging from low order models for turbulent flows to deep reinforcement learning algorithms and Bayesian experimental design for collective swimming. I hope to demonstrate that ML has the potential to augment, and possibly even transform, current lines of fluid mechanics research.
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