Structural shape optimization plays an important role in the design of wind-sensitive structures. The numerical evaluation of aerodynamic performance for each shape search and update during the optimization process typically involves significant computational costs. Accordingly, an effective shape optimization algorithm is needed. In this study, the reinforcement learning (RL) method with deep neural network (DNN)-based policy is utilized for the first time as a shape optimization scheme for aerodynamic mitigation of wind-sensitive structures. In addition, “tacit” domain knowledge is leveraged to enhance the training efficiency. Both the specific direct-domain knowledge and general cross-domain knowledge are incorporated into the deep RL-based aerodynamic shape optimizer via the transfer-learning and meta-learning techniques, respectively, to reduce the required datasets for learning an effective RL policy. Numerical examples for aerodynamic shape optimization of a tall building are used to demonstrate that the proposed knowledge-enhanced deep RL-based shape optimizer outperforms both gradient-based and gradient-free optimization algorithms.

中文翻译：

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基于知识的深度强化学习型形状优化器，用于风敏结构的空气动力学缓解

结构形状优化在风敏结构的设计中起着重要作用。在优化过程中，对每个形状搜索和更新的空气动力学性能进行数值评估通常会涉及大量的计算成本。因此，需要一种有效的形状优化算法。在这项研究中，基于深度神经网络（DNN）的策略的强化学习（RL）方法首次被用作形状优化方案，以减轻风敏感结构的空气动力。另外，利用“隐性”领域知识来提高训练效率。分别通过转移学习和元学习技术将特定的直接领域知识和一般的跨领域知识都合并到基于RL的深度空气动力学形状优化器中，减少学习有效的RL策略所需的数据集。通过对高层建筑进行空气动力学形状优化的数值示例来证明，所提出的知识增强型基于RL的深层形状优化器的性能优于基于梯度的优化算法和无梯度的优化算法。