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.

中文翻译：

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流体力学中的深度强化学习：主动流控制和形状优化的有前途的方法

近年来，人工神经网络（ANN）和深度学习在包括流体力学在内的广泛科学和技术领域中越来越受欢迎。尽管需要时间来充分掌握这些方法的潜力和局限性，但证据开始积累，表明它们在帮助解决理论上尚无最佳解决方法的问题上的潜力。在涉及涉及最佳控制和最佳设计的问题的流体力学中尤其如此。实际上，众所周知，由于涉及非线性，非凸性和高维，这些问题很难用传统方法有效解决。相比之下，深度强化学习（DRL）一种通过反复试验将经验策略教授给人工神经网络的优化方法，非常适合解决此类问题。在这篇简短的评论中，我们提供了对在流体力学中使用DRL的最新技术的见解，重点是控制和最佳设计问题。