电波科学学报2026，Vol.41Issue(1)：98-106,9.DOI:10.12265/j.cjors.2025156
基于残差增强神经网络的宽带极化转换超表面逆向设计
Inverse design of broadband polarization conversion metasurface based on residual-enhanced neural network
摘要
Abstract
The rapid development of artificial intelligence provides a customized solution for the free manipulation of electromagnetic waves by metasurfaces.This paper proposes a deep fully connected neural network model integrated with the idea of residual networks,which is used for the inverse prediction of structural parameters of broadband polarization-conversion metasurfaces from reflection coefficients.First,a three-layer metasurface unit structure is designed,and its 8 control parameters are determined.On this basis,by combining the refined parameter control idea of different metasurface structures with the efficient mapping capability of deep learning-based inverse design,an end-to-end mapping model from electromagnetic response to structural parameters is constructed.The residual connection mechanism is innovatively introduced,which effectively addresses the gradient vanishing problem in the training of deep networks.The paper focuses on elaborating the network architecture design integrated with residual connections,training strategies,and analyzes the impact of logarithmic transformation on prediction accuracy.Algorithm evaluation of the model shows that the coefficients of determination(R2)of the prediction results for all 8 structural parameters are greater than 0.9.The metasurface designed based on the predicted parameters maintains a polarization conversion ratio of over 90%across the entire frequency band of 8.8–24.4 GHz.Analysis indicates that this study provides an efficient and feasible method for the inverse design of metasurfaces,and this method can be further extended to the design of metasurfaces with more diverse functions.关键词
超表面/宽带极化转换/逆向设计/深度学习/残差网络Key words
metasurface/broadband polarization conversion/inverse design/deep learning/residual network分类
信息技术与安全科学引用本文复制引用
张伟胜,朱瑞超,闫明宝,随赛,罗恒杨,王甲富..基于残差增强神经网络的宽带极化转换超表面逆向设计[J].电波科学学报,2026,41(1):98-106,9.基金项目
国家自然科学基金项目(62401614,62201609) （62401614,62201609）
陕西省自然科学基金(2024JC-YBMS-462) （2024JC-YBMS-462）
国家自然科学基金区域创新发展联合基金项目(U24A20224)National Natural Science Foundation of China(62401614,62201609) （U24A20224）
Natural Science Foundation of Shaanxi Province(2024JC-YBMS-462) （2024JC-YBMS-462）
National Natural Science Foundation Regional Innovation and Development Joint Fund(U24A20224) （U24A20224）
