  In recent years, Artificial Neural Networks (ANNs) and Deep Learning have
become increasingly popular across a wide range of scientific and technical
fields, including Fluid Mechanics. While it will take time to fully grasp the
potentialities as well as the limitations of these methods, evidence is
starting to accumulate that point to their potential in helping solve problems
for which no theoretically optimal solution method is known. This is
particularly true in Fluid Mechanics, where problems involving optimal control
and optimal design are involved. Indeed, such problems are famously difficult
to solve effectively with traditional methods due to the combination of non
linearity, non convexity, and high dimensionality they involve. By contrast,
Deep Reinforcement Learning (DRL), a method of optimization based on teaching
empirical strategies to an ANN through trial and error, is well adapted to
solving such problems. In this short review, we offer an insight into the
current state of the art of the use of DRL within fluid mechanics, focusing on
control and optimal design problems.
