智能电磁计算的若干进展

刘彻 杨恺乔 鲍江涵 俞文明 游检卫 李廉林 崔铁军

刘彻, 杨恺乔, 鲍江涵, 等. 智能电磁计算的若干进展[J]. 雷达学报, 2023, 12(4): 657–683. doi: 10.12000/JR23133
引用本文: 刘彻, 杨恺乔, 鲍江涵, 等. 智能电磁计算的若干进展[J]. 雷达学报, 2023, 12(4): 657–683. doi: 10.12000/JR23133
LIU Che, YANG Kaiqiao, BAO Jianghan, et al. Recent progress in intelligent electromagnetic computing[J]. Journal of Radars, 2023, 12(4): 657–683. doi: 10.12000/JR23133
Citation: LIU Che, YANG Kaiqiao, BAO Jianghan, et al. Recent progress in intelligent electromagnetic computing[J]. Journal of Radars, 2023, 12(4): 657–683. doi: 10.12000/JR23133

智能电磁计算的若干进展

doi: 10.12000/JR23133
基金项目: 博士后创新人才支持计划(BX20220065),中央高校基本科研业务费专项资金(2242023K5002)
详细信息
    作者简介:

    刘 彻,博士,助理研究员,主要研究方向为计算电磁学、电磁超材料与人工智能的交叉技术

    杨恺乔,博士生,主要研究方向为电磁计算的智能和并行方法

    鲍江涵,博士生,主要研究方向为机器学习在电磁计算领域的运用以及超表面的智能化设计

    俞文明,副研究员,主要研究方向为计算电磁学算法和电磁仿真软件的国产化推进

    游检卫,教授,博士生导师,主要研究方向为计算电磁学和电磁超构材料

    李廉林,教授,博士生导师,主要研究方向为电磁感知体制、算法和工程应用

    崔铁军,中国科学院院士,主要研究方向为电磁超材料和计算电磁学

    通讯作者:

    崔铁军 tjcui@seu.edu.cn

  • 责任主编:徐丰 Corresponding Editor: XU Feng
  • 中图分类号: TN82

Recent Progress in Intelligent Electromagnetic Computing

Funds: China National Postdoctoral Program for Innovative Talents (BX20220065), The Fundamental Research Funds for the Central Universities (2242023K5002)
More Information
  • 摘要: 自19世纪建立麦克斯韦方程以来,计算电磁学经历了百年的稳定发展,现已发展出有限差分法、有限元法、矩量法等数值算法和高频近似方法,是现代电子与信息领域的重要基石。近年来,人工智能技术经历了蓬勃发展,因其强大的建模和推理能力在电磁学界崭露头角,催生出智能电磁计算这一新兴研究方向,吸引了国内外众多科研工作者致力于该领域的研究,在电磁建模与仿真、电磁新材料和器件的分析与综合、探测与感知等领域涌现出很多优秀成果,为发展百余年的电磁学注入了新鲜血液。该文讨论了智能电磁计算的若干进展,为读者入门并了解该领域最新的研究成果提供有益帮助。

     

  • 图  1  数据驱动的正向电磁计算分类

    Figure  1.  The classification of data-driven forward electromagnetic computing

    图  2  部分数据驱动正向电磁计算研究成果

    Figure  2.  Several research results of data-driven forward electromagnetic computing

    图  3  部分物理驱动以及算子学习正向计算研究成果

    Figure  3.  Several research results of PINN based and operator-learning based forward computing

    图  4  部分可微分正向电磁计算研究成果示意

    Figure  4.  Several research results of differentiable forward electromagnetic computing

    图  5  基于U-Net结构的逆向智能电磁成像

    Figure  5.  Reverse intelligent electromagnetic imaging based on U-Net

    图  6  基于Pix2Pix结构的逆向智能电磁成像

    Figure  6.  Reverse intelligent electromagnetic imaging based on Pix2Pix

    图  7  借鉴迭代优化算法的端到端逆向智能电磁成像

    Figure  7.  End-to-end reverse intelligent electromagnetic imaging inspired by iterative optimization methods

    图  8  迭代形式的逆向智能电磁成像

    Figure  8.  Iterative intelligent electromagnetic imaging

    图  9  基于点云的无网格三维逆向智能电磁成像模型[86]

    Figure  9.  A mesh-free 3-D deep learning electromagnetic inversion method based on point clouds[86]

    10  基于信息超材料的智能方向图设计

    10.  Intelligent design of radiation patterns based on information metamaterial

    图  11  基于信息超材料的非监督智能设计

    Figure  11.  Unsupervised intelligent design based on information metamaterial

    图  12  用于复杂系统的信息超材料的智能化设计

    Figure  12.  Intelligent design of information metamaterial for complex systems

    图  13  可编程人工智能机的工作原理示意图[107]

    Figure  13.  The Working Principle of Programmable Artificial Intelligence Machine (PIAM)[107]

    图  14  可编程表面等离激元神经网络[109]

    Figure  14.  Programmable Surface Plasmonic Neural Networks (SPNN)[109]

    图  15  基于信息超表面的智能室内机器人(I2MR)系统[111]

    Figure  15.  Intelligent indoor metasurface robotics (I2MR)[111]

    图  16  基于计算机视觉的智能超表面跟踪与通信系统的工作原理[113]

    Figure  16.  The working principle of intelligent metasurface system for automatic tracking of moving targets and wireless communications based on computer vision[113]

    表  1  4种智能电磁计算方法特性对比

    Table  1.   Comparison of characteristics of four intelligent electromagnetic calculation methods

    类别应用成熟度计算复杂度泛化能力训练数据量
    数据驱动(结果学习)
    数据驱动(过程学习)较高适中较好适中
    物理驱动中等较低适中
    算子学习较好较多
    可微分计算中等
    下载: 导出CSV
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  • 收稿日期:  2023-07-18
  • 修回日期:  2023-08-07
  • 网络出版日期:  2023-08-21
  • 刊出日期:  2023-08-28

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