  Deep reinforcement learning (DRL) has recently been adopted in a wide range
of physics and engineering domains for its ability to solve decision-making
problems that were previously out of reach due to a combination of
non-linearity and high dimensionality. In the last few years, it has spread in
the field of computational mechanics, and particularly in fluid dynamics, with
recent applications in flow control and shape optimization. In this work, we
conduct a detailed review of existing DRL applications to fluid mechanics
problems. In addition, we present recent results that further illustrate the
potential of DRL in Fluid Mechanics. The coupling methods used in each case are
covered, detailing their advantages and limitations. Our review also focuses on
the comparison with classical methods for optimal control and optimization.
Finally, several test cases are described that illustrate recent progress made
in this field. The goal of this publication is to provide an understanding of
DRL capabilities along with state-of-the-art applications in fluid dynamics to
researchers wishing to address new problems with these methods.
