This work presents a deep learning surrogate model for the fast simulation of
high-dimensional frequency selective surfaces. We consider unit-cells which are
built as multiple concatenated stacks of screens and their design requires the
control over many geometrical degrees of freedom. Thanks to the introduction of
physical insight into the model, it can produce accurate predictions of the
S-parameters of a certain structure after training with a reduced dataset.The
proposed model is highly versatile and it can be used with any kind of
frequency selective surface, based on either perforations or patches of any
arbitrary geometry. Numeric examples are presented here for the case of
frequency selective surfaces composed of screens with rectangular perforations,
showing an excellent agreement between the predicted performance and such
obtained with a full-wave simulator.

该论文提出了一个深度学习替代模型，用于快速模拟高维频率选择性表面。通过引入物理洞察力到模型中，它可以在使用缩小数据集进行训练后，准确预测某种结构的 S 参数。该模型非常多功能，可用于任何类型的频率选择性表面，无论是基于任意几何形状的孔洞还是贴片。通过数值示例，展示了基于矩形孔洞屏幕组成的频率选择性表面的预测性能与全波模拟器的结果之间的优秀一致性。