arXiv 2001.024642020-01-08physics.flu-dynphysics.comp-ph

流体力学中的深度强化学习：一种有前途的主动流动控制和形状优化方法

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

Jean Rabault, Feng Ren, Wei Zhang, Hui Tang, Hui Xu

摘要：

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变得越来越受欢迎。虽然要完全掌握这些方法的潜力和局限性需要时间，但证据开始积累，表明它们在帮助解决理论上没有最佳解决方法的问题方面的潜力。在涉及最优控制和最优设计的流体力学中尤其如此。事实上，这些问题是众所周知的难以有效地解决与传统的方法，由于其涉及的非线性，非凸性和高维的组合。相比之下，深度强化学习（DRL）是一种基于通过试错将经验策略教授给ANN的优化方法，非常适合解决此类问题。在这篇简短的评论中，我们提供了一个深入了解使用DRL在流体力学，专注于控制和优化设计问题的当前状态的艺术。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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