Metasurfaces (MSs) show great promise in efficient electromagnetic energy harvesting (EMEH) due to their compactness, high efficiency, and long-distance transmission capabilities. Nonetheless, the conventional iterative and time-consuming solving process of MSs significantly escalates computational demands. Furthermore, once processed, the MS shape remains fixed and cannot be adapted to changing requirements. Accordingly, a critical challenge is the development of a new efficient solver for MS real-time tuning. Here, we introduce a class of digital coded MS databases including multiple pre-defined resonant frequency MS. The combination of multiple MS base functions from the database enables swift resonance frequency adjustments to adapt to changing environmental conditions. A topology optimization method based on data-driven modeling is employed to rapidly acquire the optimal digital coding for the corresponding MS at various operating frequencies, facilitating the construction of a database. This approach integrates a convolutional neural network and genetic algorithm (CNNGA). It not only enables more accurate and expedited forward prediction of MSs' electromagnetic (EM) response but also facilitates inverse design based on specified requirements. We employ this method to design a MS that achieves perfect energy harvesting (EH) over a broad range of incident angles and polarization directions. In addition, a data-driven modeling is used to establish an EH efficiency predictive model corresponding to MS combination. This model serves as a guide for real-time MS adjustments as per changing requirements. Compared to previously designed MSs, this model achieves rapid design and adaptive adjustment capabilities. Through the incorporation of various functional MS base functions into the database, this method can be universally applied to MS combinations tailored to specific functions, including EM cloaking, ultra-thin flat lenses, and computational MSs.

中文翻译：

---

数字编码超表面数据库的数据驱动建模和快速调整：在自适应电磁能量收集中的应用

超表面（MS）由于其紧凑、高效和长距离传输能力，在高效电磁能量收集（EMEH）方面显示出巨大的前景。尽管如此，MS 传统的迭代且耗时的求解过程显着增加了计算需求。此外，一旦加工完成，MS 的形状就保持固定，无法适应不断变化的需求。因此，一个关键的挑战是开发一种用于 MS 实时调谐的新型高效求解器。在这里，我们介绍一类数字编码 MS 数据库，包括多个预定义的共振频率 MS。数据库中的多个 MS 基本功能的组合可以快速调整共振频率，以适应不断变化的环境条件。采用基于数据驱动建模的拓扑优化方法，快速获取不同工作频率下对应MS的最优数字编码，方便数据库的构建。该方法集成了卷积神经网络和遗传算法（CNNGA）。它不仅能够更准确、更快速地对 MS 的电磁 (EM) 响应进行正向预测，而且还有助于根据指定要求进行逆向设计。我们采用这种方法设计了一款 MS，该 MS 可在较宽的入射角和偏振方向范围内实现完美的能量收集 (EH)。此外，利用数据驱动建模建立了与MS组合相对应的EH效率预测模型。该模型可作为根据不断变化的需求进行实时 MS 调整的指南。与之前设计的MS相比，该模型实现了快速设计和自适应调整能力。通过将各种功能性MS基本功能纳入数据库，该方法可以普遍应用于针对特定功能定制的MS组合，包括电磁隐身、超薄平面透镜和计算MS。