Cascaded Metasurface Design Using Electromagnetic Inversion With Gradient-Based Optimization

被引:10
|
作者
Brown, Trevor [1 ]
Mojabi, Puyan [1 ]
机构
[1] Univ Manitoba, Dept Elect & Comp Engn, Winnipeg, MB R3T 5V6, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Metasurfaces; Electromagnetics; Optimization; Design methodology; Magnetic separation; Surface impedance; Magnetic susceptibility; Electromagnetic metasurfaces; inverse problems; inverse source problems; optimization; pattern synthesis; BIANISOTROPIC HUYGENS METASURFACE; SOURCE RECONSTRUCTION; FIELD; CAVITY;
D O I
10.1109/TAP.2021.3119115
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This article presents an electromagnetic inversion algorithm for the design of cascaded metasurfaces that enables the design process to begin from more practical output field specifications, such as a desired power pattern or far-field (FF) performance criteria. Thus, this method combines the greater field transformation support of multiple metasurfaces with the flexibility of the electromagnetic inverse source framework. To this end, two optimization problems are formed: one associated with the interior space between two metasurfaces and the other for the exterior space. The cost functionals corresponding to each of these two optimization problems are minimized using the nonlinear conjugate gradient (CG) algorithm with analytic expressions for the gradient operators. The numerical implementation of the developed design procedure is presented in detail, including a total variation (TV) regularizer that is incorporated into the optimization procedure to favor smooth field variations from one unit cell to the next. The capabilities of the method are demonstrated by converting the produced surface susceptibilities into three-layer admittance sheet models, which are simulated in several 2-D examples.
引用
收藏
页码:2033 / 2045
页数:13
相关论文
共 50 条
  • [41] MAMGD: Gradient-Based Optimization Method Using Exponential Decay
    Sakovich, Nikita
    Aksenov, Dmitry
    Pleshakova, Ekaterina
    Gataullin, Sergey
    [J]. TECHNOLOGIES, 2024, 12 (09)
  • [42] Spacecraft Attitude Motion Planning Using Gradient-Based Optimization
    Celani, Fabio
    Lucarelli, Dennis
    [J]. JOURNAL OF GUIDANCE CONTROL AND DYNAMICS, 2020, 43 (01) : 140 - 145
  • [43] A gradient-based direct aperture optimization
    Yang, Jie
    Zhang, Pengcheng
    Zhang, Liyuan
    Gui, Zhiguo
    [J]. Shengwu Yixue Gongchengxue Zazhi/Journal of Biomedical Engineering, 2018, 35 (03): : 358 - 367
  • [44] Catalyst for Gradient-based Nonconvex Optimization
    Paquette, Courtney
    Lin, Hongzhou
    Drusvyatskiy, Dmitriy
    Mairal, Julien
    Harchaoui, Zaid
    [J]. INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 84, 2018, 84
  • [45] On using a gradient-based method for heliostat field layout optimization
    Lutchman, S. L.
    Groenwold, A. A.
    Gauche, P.
    Bode, S.
    [J]. PROCEEDINGS OF THE SOLARPACES 2013 INTERNATIONAL CONFERENCE, 2014, 49 : 1429 - 1438
  • [46] A skeletonization algorithm for gradient-based optimization
    Menten, Martin J.
    Paetzold, Johannes C.
    Zimmer, Veronika A.
    Shit, Suprosanna
    Ezhov, Ivan
    Holland, Robbie
    Probst, Monika
    Schnabel, Julia A.
    Rueckert, Daniel
    [J]. 2023 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2023), 2023, : 21337 - 21346
  • [47] ON THE ADAPTIVITY OF STOCHASTIC GRADIENT-BASED OPTIMIZATION
    Lei, Lihua
    Jordan, Michael I.
    [J]. SIAM JOURNAL ON OPTIMIZATION, 2020, 30 (02) : 1473 - 1500
  • [48] Solving Optimization Problems Using an Extended Gradient-Based Optimizer
    Ewees, Ahmed A.
    [J]. MATHEMATICS, 2023, 11 (02)
  • [49] Trivializations for Gradient-Based Optimization on Manifolds
    Lezcano-Casado, Mario
    [J]. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), 2019, 32
  • [50] Rational catalyst design for CO oxidation: a gradient-based optimization strategy
    Wang, Ziyun
    Hu, P.
    [J]. CATALYSIS SCIENCE & TECHNOLOGY, 2021, 11 (07) : 2604 - 2615