Stochastic PEEC Method Based on Polynomial Chaos Expansion

被引:7
|
作者
Torchio, R. [1 ]
Di Rienzo, L. [2 ]
Codecasa, L. [2 ]
机构
[1] Univ Padua, Dipartimento Ingn Ind, I-35141 Padua, Italy
[2] Politecn Milan, Dipartimento Elettron Informaz & Bioingn, I-20133 Milan, Italy
关键词
Integral equations; partial element equivalent circuit (PEEC); polynomial chaos expansion (PCE); uncertainty quantification;
D O I
10.1109/TMAG.2019.2908588
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
The new stochastic partial element equivalent circuit (PEEC) method is proposed for uncertainty quantification in electromagnetic problems when material parameters are considered as random variables. The proposed formulation is derived using polynomial chaos expansion (PCE) and Galerkin projection. For the first time, the well-known advantages of PEEC are combined with those of PCE techniques, which can tackle also large variations in the random parameters. Volume conductive media are first considered in the formulation, which is then extended to dielectric, magnetic, and surface conductive media.
引用
收藏
页数:4
相关论文
共 50 条
  • [1] MEMS stochastic model order reduction method based on polynomial chaos expansion
    Youping Gong
    Xiangjuan Bian
    Chen Guojin
    Lv Yunpeng
    Zhangming Peng
    Microsystem Technologies, 2016, 22 : 993 - 1003
  • [2] MEMS stochastic model order reduction method based on polynomial chaos expansion
    Gong, Youping
    Bian, Xiangjuan
    Chen Guojin
    Lv Yunpeng
    Peng, Zhangming
    MICROSYSTEM TECHNOLOGIES-MICRO-AND NANOSYSTEMS-INFORMATION STORAGE AND PROCESSING SYSTEMS, 2016, 22 (05): : 993 - 1003
  • [3] Uncertainty Quantification in PEEC Method: A Physics-Informed Neural Networks-Based Polynomial Chaos Expansion
    Ping, Yuan
    Zhang, Yanming
    Jiang, Lijun
    PROCEEDINGS OF THE 2024 IEEE JOINT INTERNATIONAL SYMPOSIUM ON ELECTROMAGNETIC COMPATIBILITY, SIGNAL & POWER INTEGRITY: EMC JAPAN/ASIAPACIFIC INTERNATIONAL SYMPOSIUM ON ELECTROMAGNETIC COMPATIBILITY, EMC JAPAN/APEMC OKINAWA 2024, 2024, : 395 - 398
  • [4] Uncertainty Quantification in PEEC Method: A Physics-Informed Neural Networks-Based Polynomial Chaos Expansion
    Ping, Yuan
    Zhang, Yanming
    Jiang, Lijun
    IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY, 2024, 66 (06) : 2095 - 2101
  • [5] Stochastic Finite Element Method Using Polynomial Chaos Expansion
    Zhao, W.
    Liu, J. K.
    ADVANCES IN MECHANICAL DESIGN, PTS 1 AND 2, 2011, 199-200 : 500 - +
  • [6] A reduced polynomial chaos expansion method for the stochastic finite element analysis
    Pascual, B.
    Adhikari, S.
    SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES, 2012, 37 (03): : 319 - 340
  • [7] A reduced polynomial chaos expansion method for the stochastic finite element analysis
    B PASCUAL
    S ADHIKARI
    Sadhana, 2012, 37 : 319 - 340
  • [8] Stochastic Thermal Modeling by Polynomial Chaos Expansion
    Codecasa, Lorenzo
    Di Rienzo, Luca
    2013 19TH INTERNATIONAL WORKSHOP ON THERMAL INVESTIGATIONS OF ICS AND SYSTEMS (THERMINIC), 2013, : 33 - 38
  • [9] Stochastic Economic Dispatch with Advanced Polynomial Chaos Expansion
    Rawal, Keerti
    Ahmad, Aijaz
    IFAC PAPERSONLINE, 2024, 57 : 155 - 160
  • [10] Solution of a stochastic Darcy equation by polynomial chaos expansion
    Shalimova I.A.
    Sabelfeld K.K.
    Numerical Analysis and Applications, 2017, 10 (3) : 259 - 271