This paper introduces a novel machine learning-based optimization strategy for multi-functional acoustic black hole (ABH) metaplates. The primary objective is to achieve a multi-functional metaplate with excellent performance in elastic wave attenuation and load-bearing capacity simultaneously. The paper begins by describing the design of nanocomposite ABH metaplates, presenting a new pathway to realize multi-functional metaplates. Then, a semi-analytical method, based on plate theory and the Bloch–Floquet theorem, is introduced to consider the band structure of the nanocomposite metaplates. Through systematic analysis, the impacts of the ABH effect, nanocomposite reinforcements, and the viscoelastic damping layer on the bandgaps and strain energy compliance are highlighted. Meanwhile, two optimization objectives representing bandgap characteristics and in-plane stiffness are derived respectively. Subsequently, a deep learning surrogate model is employed to establish a relationship involving significant parameters with the optimization objectives. The performance evaluation confirms accuracy and computational speed of the surrogate model. Finally, an optimization strategy based on deep reinforcement learning is proposed to obtain multi-functional metaplates with superior bandgaps, enhanced in-plane stiffness, or both. The robustness and efficiency of the strategy are demonstrated under different tests. The results show that the proposed strategy can achieve identical results as the genetic algorithm and nondominated sorting genetic algorithm-II, while surpassing them in computational efficiency and balancing multiple objectives. The findings of this study serve as valuable references for the future development and application of multi-functional advanced metamaterials.

中文翻译：

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基于机器学习的声学黑洞元板优化设计，以增强带隙和承载能力

本文介绍了一种新颖的基于机器学习的多功能声学黑洞（ABH）元板的优化策略。主要目标是实现同时具有优异的弹性波衰减和承载能力的多功能金属板。本文首先描述了纳米复合材料 ABH 超板的设计，提出了实现多功能超板的新途径。然后，引入基于板理论和Bloch-Floquet定理的半解析方法来考虑纳米复合材料超板的能带结构。通过系统分析，强调了ABH效应、纳米复合材料增强体和粘弹性阻尼层对带隙和应变能柔量的影响。同时，分别推导了代表带隙特性和面内刚度的两个优化目标。随后，采用深度学习代理模型来建立涉及重要参数与优化目标的关系。性能评估确认了代理模型的准确性和计算速度。最后，提出了一种基于深度强化学习的优化策略，以获得具有优异带隙、增强面内刚度或两者兼而有之的多功能元板。该策略的稳健性和效率在不同的测试中得到了证明。结果表明，该策略可以达到与遗传算法和非支配排序遗传算法-II相同的结果，但在计算效率和平衡多目标方面优于它们。该研究结果为多功能先进超材料的未来开发和应用提供了有价值的参考。