论文标题
流体力学中的深度加固学习：一种有前途的活跃流控制和形状优化的方法
Deep Reinforcement Learning in Fluid Mechanics: a promising method for both Active Flow Control and Shape Optimization
论文作者
论文摘要
近年来，人工神经网络（ANN）和深度学习变得越来越流行，包括流体力学在内的广泛的科学和技术领域。尽管这些方法的潜力和局限性将需要时间花费时间，但证据开始积累了这一点，即它们在帮助解决理论上最佳解决方案方法的潜力方面的潜力。在流体力学中尤其如此，其中涉及最佳控制和最佳设计的问题。实际上，由于非线性，非凸度和高维度的结合，因此很难用传统方法有效解决此类问题。相比之下，深度强化学习（DRL）是一种基于通过反复试验的ANN教学策略进行教学的优化方法，非常适合解决此类问题。在这篇简短的评论中，我们深入了解了在流体力学中使用DRL的当前艺术状态，重点是控制和最佳设计问题。
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.
