基于智能算法的超材料快速优化设计方法研究进展

贾宇翔,  王甲富,  陈维,  随赛,  朱瑞超,  邱天硕,  李勇峰,  韩亚娟,  屈绍波

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引用本文: 贾宇翔, 王甲富, 陈维, 等. 基于智能算法的超材料快速优化设计方法研究进展[J]. 雷达学报, 2021, 10(2): 220–239. doi: 10.12000/JR21027
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Citation: JIA Yuxiang, WANG Jiafu, CHEN Wei, et al. Research progress on rapid optimization design methods of metamaterials based on intelligent algorithms[J]. Journal of Radars, 2021, 10(2): 220–239. doi: 10.12000/JR21027
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基于智能算法的超材料快速优化设计方法研究进展

DOI: 10.12000/JR21027
  • 1.

    空军工程大学基础部 西安 710051

  • 2.

    93704部队 北京 101100

基金项目: 国家自然科学基金(61971435, 61971341, 61801509, 61901508)，科技部国家重点研发计划(2017YFA0700201)
详细信息
    作者简介:

    贾宇翔(1993–)，男，河北衡水人，2016年在空军工程大学获电子科学与技术专业硕士学位，现在空军工程大学基础部攻读博士学位，主要研究方向为基于人工表面等离激元的电磁散射调控。E-mail: jiayuxiang93@163.com

    王甲富(1981–)，男，山东聊城人，教授，博士生导师，博士学位论文获2012年“全国优秀博士学位论文”提名，荣立个人三等功三次。主要研究方向为超材料设计及其在微波器件中的应用，目前已发表学术论文390余篇。E-mail: wangjifu1981@126.com

    陈　维(1982–)，男，陕西三原人，2009年在空军工程大学导弹学院军事装备学获硕士学位，现为93704部队任雷达工程师，主要研究方向为制导雷达指控仓。E-mail: chenwei918113@126.com

    随　赛(1993–)，男，安徽亳州人，博士，讲师，2019年毕业于空军工程大学，现任职于空军工程大学，主要研究方向为隐身新材料与新技术，目前已发表学术论文35篇。E-mail: suisai_mail@foxmail.com

    朱瑞超(1996–)，男，山东济南人，2020年在空军工程大学获电子科学与技术专业硕士学位，现在空军工程大学基础部攻读博士学位。主要研究方向为基于智能算法的超表面设计。E-mail: zhuruichao1996@163.com

    邱天硕(1992–)，男，吉林长春人，博士，讲师。2019年于空军工程大学获得博士学位，现任空军工程大学基础部讲师。主要研究方向为智能材料与设计、有源超材料等。E-mail: qiutianshuo1992@163.com

    李勇峰(1986–)，男，甘肃平凉人，2010年在空军工程大学获物理电子学硕士学位，2015年在空军工程大学获物理电子学博士学位。现为空军工程大学基础部讲师，主要研究方向为电磁超构表面、天线、隐身材料与隐身技术、雷达目标特性仿真评估等，目前已发表学术论文40余篇。E-mail: liyf217130@126.com

    韩亚娟 (1989–)，女，陕西礼泉人，博士，讲师。2020年获西安电子科技大学博士学位，现担任空军工程大学基础部讲师，主要研究方向为超表面、人工表面等离激元及其在天线设计中的应用。目前已发表学术论文30余篇。E-mail: mshyj_mail@126.com

    屈绍波(1965–)，男，安徽亳州人，教授，博士生导师，全国模范教师，全军学科拔尖人才，空军级专家，享受政府特殊津贴，荣立个人二等功一次。主要研究方向为材料物理、超材料，目前已发表学术论文490余篇。承担各类课题60余项，国家自然科学基金项目20余项。E-mail: qushaobo@mail.xjtu.edu.cn

    通讯作者:

    王甲富 wangjifu1981@126.com

  • 责任主编：李龙 Corresponding Editor: LI Long

  • 中图分类号: TB34

Research Progress on Rapid Optimization Design Methods of Metamaterials Based on Intelligent Algorithms

  • 1.

    Department of Basic Sciences, Air Force Engineering University, Xi’an 710051, China

  • 2.

    Unit 93704 of PLA, Beijing 101100, China

Funds: The National Natural Science Foundation of China (61971435, 61971341, 61801509, 61901508), The National Key Research and Development Program of China (2017YFA0700201)
More Information

摘要

    摘要
  • 摘要: 目前，超材料研究不断向工程化应用推进，在物理机理与效应、设计理论与方法、加工制备与测试等方面取得了突飞猛进的发展。但是，传统的超材料设计主要依赖人工设计和优化，面对大规模的工程化应用设计时，无法实现数量庞大的超材料结构单元的快速整体设计。近几年，涵盖传统启发式算法和神经网络算法的智能算法在超材料设计中所占的比重逐步上升，基于智能算法设计超材料能够打破传统设计方法在不同基材体系、不同频段以及不同性能指标下设计的局限性，展现出快速设计和架构创新的独特优势。该文综述了包括遗传算法、Hopfield网络算法和深度学习在内的几种典型智能算法在超材料设计中的应用，包括正向设计方法和逆向设计方法。基于智能算法能够实现不同性能指标的频率选择表面、多机理复合吸波超材料、平板聚焦超表面以及异常反射超表面的快速设计，为推动超材料技术的工程化应用提供必要设计手段支撑。

     

    Abstract: At present, research on metamaterials is continuously advancing to engineering applications, and great progress is being achieved in the areas of physical mechanisms and effects, design theory and methods, and fabrication and measurement. However, traditional metamaterials design mainly relies on artificial design and optimization. In the face of large-scale engineering applications, it is impossible to realize the rapid overall design of a large number of metamaterial structural units. In recent years, the proportion of intelligent algorithms covering traditional heuristic algorithms and neural network algorithms in metamaterials design has increased gradually. Metamaterials design based on intelligent algorithms can surpass the limitation of traditional methods in different substrate systems, frequency variation, and different performance indicators, offering the unique advantages of rapid design and architectural innovation. This paper summarizes the application of several typical intelligent algorithms, including the genetic algorithm, Hopfield network algorithm, and deep learning algorithm in metamaterials design, which include forward designs and an inverse design. The use of intelligent algorithms can achieve the rapid design of frequency selective surfaces under different performance indexes, multi-mechanisms composite absorber metamaterials, flat focusing, and abnormal reflection metasurfaces, providing the necessary support for design methods while promoting the engineering applications of metamaterials.

     

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  • 图  1  神经网络应用方法

    Figure  1.  Application methods of neural network

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    图  2  拓扑构型FSS[23]

    Figure  2.  Topological FSS[23]

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    图  3  优化后的带通型FSS[23]

    Figure  3.  The optimized bandpass FSS[23]

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    图  4  宽带吸波复合材料拓扑构型[23]

    Figure  4.  Schematic illustration of wideband absorber topology configuration[23]

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    图  5  吸收率随极化方式和入射角变化[23]

    Figure  5.  Variation of absorptivity with polarization mode and incident angle[23]

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    图  6  基于拓扑优化设计的超表面吸波体

    Figure  6.  Metaurface absorber based on topology optimization design

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    图  7  对称性破缺超表面吸波体结构示意图[29]

    Figure  7.  Structure illustration of the symmetry broken metasurface absorber[29]

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    图  8  金属板与超表面的对比[29]

    Figure  8.  Comparisons between metal plate and metasurfaces[29]

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    图  9  低RCS编码超表面[30]

    Figure  9.  Low RCS coded metasurface[30]

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    图  10  RCS远场图[30]

    Figure  10.  RCS far-fields[30]

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    图  11  拓扑结构单元[23]

    Figure  11.  Topology unit[23]

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    图  12  编码超表面[23]

    Figure  12.  Coded metasurface[23]

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    图  13  超表面样品[23]

    Figure  13.  Metasurface sample[23]

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    图  14  基于遗传算法的优化流程示意图[37]

    Figure  14.  Optimization flow illustration based on genetic algorithm[37]

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    图  15  曲线与金属线形[37]

    Figure  15.  Curve and metal line shape[37]

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    图  16  吸波体的电磁响应[37]

    Figure  16.  Electromagnetic response of the absorber[37]

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    图  17  Hopfield网络[41]

    Figure  17.  Hopfield network[41]

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    图  18  Hopfield网络的联想记忆功能[41]

    Figure  18.  Associative memory function of Hopfield network[41]

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    图  19  异常反射超表面[41]

    Figure  19.  Abnormal reflection metasurface[41]

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    图  20  基于深度学习-遗传算法复合优化流程图[48]

    Figure  20.  Flow chart of composite optimization based on deep learning and genetic algorithm[48]

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    图  21  Inception V3网络结构[46]

    Figure  21.  Network structure of Inception V3[46]

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    图  22  超表面电磁性能[46]

    Figure  22.  Electromagnetic performances of metasureface[46]

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    表  1  不同智能算法在超材料优化设计中优缺点对比

    Table  1.   Comparison of advantages and disadvantages of different intelligent algorithms in metamaterial optimization designs

    算法类型优点缺点
    启发式算法[9—12]全局搜索能力强，高度自定义，兼容性高计算速度慢，易陷入局部最优解
    机器学习[13—16,21—23]学习能力强，适应性好，领域优势明显训练成本高，硬件要求高，模型设计复杂
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