The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data
from experiments, field measurements, and large-scale simulations at multiple
spatiotemporal scales. Machine learning (ML) offers a wealth of techniques to extract
information from data that can be translated into knowledge about the underlying fluid
mechanics. Moreover, ML algorithms can augment domain knowledge and automate tasks
related to flow control and optimization. This article presents an overview of past history …