Accelerating multilayer frequency selective surface absorber design by combining equivalent circuit models with machine learning

被引:0
作者
Zhao, Yong [1 ,2 ]
Cao, Zhaowang [1 ,2 ]
Cao, Qiyang [1 ,2 ]
Li, Haoran [1 ,2 ]
Ji, Xiao [1 ,2 ]
Miao, Ling [1 ,2 ]
Jiang, Jianjun [1 ,2 ]
机构
[1] Huazhong Univ Sci & Technol, Sch Opt & Elect Informat, Wuhan 430074, Peoples R China
[2] Huazhong Univ Sci & Technol, Wuhan Natl Lab Optoelect, Wuhan 430074, Peoples R China
来源
ENGINEERING RESEARCH EXPRESS | 2024年 / 6卷 / 02期
基金
中国国家自然科学基金;
关键词
circuit analog absorber; frequency selective surface; equivalent circuit model; reinforcement learning; transfer learning; EFFICIENT; FSS;
D O I
10.1088/2631-8695/ad5416
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Designing high-performance electromagnetic functional materials and artificial structures is of great significance for electromagnetic wave modulation applications. Optimizing performance in the high-dimensional parameter space of electromagnetic functional materials has posed a challenging problem. This paper proposes the use of reinforcement and transfer learning methods to facilitate the quick and accurate design of wideband circuit analog absorbers (CAA). A trained reinforcement learning network is utilized to comprehend the relationship between reflectivity performance and changes in impedance parameter. Furthermore, the transfer learning method is applied to accelerate the training process, leading in a 20-fold reduction in the number of simulations. To validate the effectiveness of this approach, a double-layer absorber aiming for reflectivity less than -20 dB at 3-10 GHz is designed, requiring only 50 simulations. This demonstrates that the proposed method provides a flexible strategy for the interactive design of equivalent circuit and electromagnetic structural parameters. Moreover, it presents a novel and efficient solution for microwave absorber design, with potential applications across various microwave design fields.
引用
收藏
页数:8
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