Metasurfaces have developed rapidly with the extraordinary electromagnetic properties in electromagnetic wave control in recent years. However, the conventional metasurfaces design based on the Method of Moments (MOM), Finite Element Method (FEM) and Finite Integration Technique (FIT) are still time-consuming and demand significant computation. In this paper, we proposed a polynomial regression of standardized K-nearest neighbor algorithm (PS-KNN). The trained model shows an excellent prediction ability, the means square error (MSE) of the forward model is only 3.463 × 10 ⁻⁶ . We further report a reverse model based on forwarding prediction, which automatically constructs and optimizes the meta-atom by standardizing the electromagnetic properties (amplitude, phase, etc.) of the metasurface as the input of characteristic parameters. The MSE of the reverse model is 1.589 × 10 ⁻³ . Finally, we cascade the two models, and predicted successfully eight meta-atoms by the closed-loop network and arrange them into a focused array. The results demonstrate the algorithm model avoids extensive modeling operations and numerical calculation and over 300 times faster than traditional electromagnetic simulation software. It offers a novel effective methodology to accelerate the on-demand design of complex metasurfaces and optical structures.

中文翻译：

---

用于按需快速构建超表面的机器学习方法


近年来，超表面以其非凡的电磁特性在电磁波控制方面得到了迅速发展。然而，基于矩量法（MOM）、有限元法（FEM）和有限积分技术（FIT）的传统超表面设计仍然耗时且需要大量计算。在本文中，我们提出了标准化 K 最近邻算法（PS-KNN）的多项式回归。训练后的模型表现出优异的预测能力，前向模型的均方误差（MSE）仅为3.463×10 ^-6 。我们进一步报告了一种基于转发预测的逆向模型，该模型通过标准化超表面的电磁特性（幅度、相位等）作为特征参数的输入来自动构造和优化超原子。逆向模型的MSE为1.589 × 10 ^-3 。最后，我们将两个模型级联，并通过闭环网络成功预测了八个元原子并将它们排列成聚焦阵列。结果表明，该算法模型避免了大量的建模操作和数值计算，比传统电磁仿真软件快300倍以上。它提供了一种新颖有效的方法来加速复杂超表面和光学结构的按需设计。