Cascaded Metasurface Design Using Electromagnetic Inversion With Gradient-Based Optimization

被引:15
|
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
  • [21] Gradient-based learning and optimization
    Cao, XR
    PROCEEDINGS OF THE 17TH INTERNATIONAL SYMPOSIUM ON COMPUTER AND INFORMATION SCIENCES, 2003, : 3 - 7
  • [22] Gradient-based simulation optimization
    Kim, Sujin
    PROCEEDINGS OF THE 2006 WINTER SIMULATION CONFERENCE, VOLS 1-5, 2006, : 159 - 167
  • [23] Gradient-based optimization of filters using FDTD software
    Kozakowski, P
    Mrozowski, M
    IEEE MICROWAVE AND WIRELESS COMPONENTS LETTERS, 2002, 12 (10) : 389 - 391
  • [24] Reliability-Based Multidisciplinary Design Optimization Using Probabilistic Gradient-Based Transformation Method
    Lin, Po Ting
    Gea, Hae Chang
    JOURNAL OF MECHANICAL DESIGN, 2013, 135 (02)
  • [25] Multiobjective optimization using an aggregative gradient-based method
    Izui, Kazuhiro
    Yamada, Takayuki
    Nishiwaki, Shinji
    Tanaka, Kazuto
    STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2015, 51 (01) : 173 - 182
  • [26] Multiobjective optimization using an aggregative gradient-based method
    Kazuhiro Izui
    Takayuki Yamada
    Shinji Nishiwaki
    Kazuto Tanaka
    Structural and Multidisciplinary Optimization, 2015, 51 : 173 - 182
  • [27] Gradient-based optimization using parametric sensitivity macromodels
    Chemmangat, Krishnan
    Ferranti, Francesco
    Dhaene, Tom
    Knockaert, Luc
    INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS, 2012, 25 (04) : 347 - 361
  • [28] A Gradient-based Optimization Method for Natural Laminar Flow Design
    Hanifi, A.
    Amoignon, O.
    Pralits, J. O.
    Chevalier, M.
    SEVENTH IUTAM SYMPOSIUM ON LAMINAR-TURBULENT TRANSITION, 2010, 18 : 3 - 10
  • [29] A Gradient-based Sequential Multifidelity Approach to Multidisciplinary Design Optimization
    Wu, Neil
    Mader, Charles A.
    Martins, Joaquim R. R. A.
    STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2022, 65 (04)
  • [30] Gradient-based multidisciplinary design optimization of an autonomous underwater vehicle
    Chen, Xu
    Wang, Peng
    Zhang, Daiyu
    Dong, Huachao
    APPLIED OCEAN RESEARCH, 2018, 80 : 101 - 111