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In recent years, Artificial Neural Networks (ANNs) and Deep Learning havebecome increasingly popular across a wide range of scientific and technicalfields, including Fluid Mechanics. While it will take time to fully grasp thepotentialities as well as the limitations of these methods, evidence isstarting to accumulate that point to their potential in helping solve problemsfor which no theoretically optimal solution method is known. This isparticularly true in Fluid Mechanics, where problems involving optimal controland optimal design are involved. Indeed, such problems are famously difficultto solve effectively with traditional methods due to the combination of nonlinearity, non convexity, and high dimensionality they involve. By contrast,Deep Reinforcement Learning (DRL), a method of optimization based on teachingempirical strategies to an ANN through trial and error, is well adapted tosolving such problems. In this short review, we offer an insight into thecurrent state of the art of the use of DRL within fluid mechanics, focusing oncontrol and optimal design problems.

Deep Reinforcement Learning in Fluid Mechanics: a promising method for both Active Flow Control and Shape Optimization