The burgeoning field of metamaterials and metasurfaces has been significantly propelled by the integration of deep learning (DL) techniques, enabling a rapid artificial design with tailored exotic properties. However, the DL-based inverse design strategies frequently face reliability issues when dealing with limited sample datasets. To overcome this challenge, we propose a few-sample information-enhanced inverse design framework specifically developed for the efficient design of columnar elastic metasurfaces, to fulfill customized transmission modulation requirements. The novelty of our approach lies in developing an information-enhanced convolutional neural network (IECNN) integrating substructure combinations, stacking effects, and CBAM, which provide more comprehensive and refined input data to substantially improve the prediction performance and generalization capability. So, the IECNN can precisely replicate FEM transmission calculations with about the 10 times computational speedup using the few-sample, significantly reducing computational time and resources. By integrating IECNN with a genetic algorithm, an automated inverse design framework is established to yield the metasurface structure with specified target transmission performance in only 3.5 min. Various numerical simulations and experimental measurements demonstrate its practicality and effectiveness. Furthermore, the physical mechanism behind the customized transmission properties is elucidated to offer deeper insights into the design process. Our approach not only ensures reliable and superior design outcomes but also diminishes the dependence on extensive labeled datasets, presenting a pragmatic framework for metasurface inverse design, particularly valuable in few-sample scenarios.

中文翻译：

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用于定制传输调制弹性超表面的少样本信息增强逆向设计框架


深度学习 (DL) 技术的集成极大地推动了超材料和超表面领域的蓬勃发展，实现了具有定制奇异特性的快速人工设计。然而，基于深度学习的逆向设计策略在处理有限的样本数据集时经常面临可靠性问题。为了克服这一挑战，我们提出了一种专门为柱状弹性超表面的高效设计而开发的少样本信息增强逆向设计框架，以满足定制的传输调制要求。我们方法的新颖之处在于开发了一种集成子结构组合、堆叠效应和 CBAM 的信息增强卷积神经网络（IECNN），它提供更全面和更精细的输入数据，从而显着提高预测性能和泛化能力。因此，IECNN 可以使用少量样本精确复制 FEM 传输计算，计算速度提高约 10 倍，从而显着减少计算时间和资源。通过将IECNN与遗传算法相结合，建立了自动逆向设计框架，只需3.5分钟即可产生具有指定目标传输性能的超表面结构。各种数值模拟和实验测量证明了其实用性和有效性。此外，还阐明了定制传输特性背后的物理机制，以便为设计过程提供更深入的见解。我们的方法不仅确保了可靠和卓越的设计结果，而且减少了对大量标记数据集的依赖，为超表面逆设计提供了一个实用的框架，在少量样本的情况下特别有价值。