Statistical Estimation of Crosstalk through a Modified Stochastic Reduced Order Model Approach

Tao LIANG; Flavia GRASSI; Giordano SPADACINI; Sergio Amedeo PIGNARI

doi:10.1587/transcom.2017EBP3140

Statistical Estimation of Crosstalk through a Modified Stochastic Reduced Order Model Approach

Tao LIANG, Flavia GRASSI, Giordano SPADACINI, Sergio Amedeo PIGNARI

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Summary :

This work presents a hybrid formulation of the stochastic reduced order model (SROM) algorithm, which makes use of Gauss quadrature, a key ingredient of the stochastic collocation method, to avoid the cumbersome optimization process required by SROM for optimal extraction of the sample set. With respect to classic SROM algorithms, the proposed formulation allows a significant reduction in computation time and burden as well as a remarkable improvement in the accuracy and convergence rate in the estimation of statistical moments. The method is here applied to a specific case study, that is the prediction of crosstalk in a two-conductor wiring structure with electrical and geometrical parameters not perfectly known. Both univariate and multivariate analyses are carried out, with the final objective being to compare the performance of the two SROM formulations with respected to Monte Carlo simulations.

Publication: IEICE TRANSACTIONS on Communications Vol.E101-B No.4 pp.1085-1093

Publication Date: 2018/04/01

Publicized: 2017/09/28

Online ISSN: 1745-1345

DOI: 10.1587/transcom.2017EBP3140

Type of Manuscript: PAPER

Category: Electromagnetic Compatibility(EMC)

Authors

Tao LIANG
  Politecnico di Milano
Flavia GRASSI
  Politecnico di Milano
Giordano SPADACINI
  Politecnico di Milano
Sergio Amedeo PIGNARI
  Politecnico di Milano

Keyword

uncertainty quantification, stochastic reduced order model, Gaussian quadrature, crosstalk

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Tao LIANG, Flavia GRASSI, Giordano SPADACINI, Sergio Amedeo PIGNARI, "Statistical Estimation of Crosstalk through a Modified Stochastic Reduced Order Model Approach" in IEICE TRANSACTIONS on Communications, vol. E101-B, no. 4, pp. 1085-1093, April 2018, doi: 10.1587/transcom.2017EBP3140.
Abstract: This work presents a hybrid formulation of the stochastic reduced order model (SROM) algorithm, which makes use of Gauss quadrature, a key ingredient of the stochastic collocation method, to avoid the cumbersome optimization process required by SROM for optimal extraction of the sample set. With respect to classic SROM algorithms, the proposed formulation allows a significant reduction in computation time and burden as well as a remarkable improvement in the accuracy and convergence rate in the estimation of statistical moments. The method is here applied to a specific case study, that is the prediction of crosstalk in a two-conductor wiring structure with electrical and geometrical parameters not perfectly known. Both univariate and multivariate analyses are carried out, with the final objective being to compare the performance of the two SROM formulations with respected to Monte Carlo simulations.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2017EBP3140/_p

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@ARTICLE{e101-b_4_1085,
author={Tao LIANG, Flavia GRASSI, Giordano SPADACINI, Sergio Amedeo PIGNARI, },
journal={IEICE TRANSACTIONS on Communications},
title={Statistical Estimation of Crosstalk through a Modified Stochastic Reduced Order Model Approach},
year={2018},
volume={E101-B},
number={4},
pages={1085-1093},
abstract={This work presents a hybrid formulation of the stochastic reduced order model (SROM) algorithm, which makes use of Gauss quadrature, a key ingredient of the stochastic collocation method, to avoid the cumbersome optimization process required by SROM for optimal extraction of the sample set. With respect to classic SROM algorithms, the proposed formulation allows a significant reduction in computation time and burden as well as a remarkable improvement in the accuracy and convergence rate in the estimation of statistical moments. The method is here applied to a specific case study, that is the prediction of crosstalk in a two-conductor wiring structure with electrical and geometrical parameters not perfectly known. Both univariate and multivariate analyses are carried out, with the final objective being to compare the performance of the two SROM formulations with respected to Monte Carlo simulations.},
keywords={},
doi={10.1587/transcom.2017EBP3140},
ISSN={1745-1345},
month={April},}

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TY - JOUR
TI - Statistical Estimation of Crosstalk through a Modified Stochastic Reduced Order Model Approach
T2 - IEICE TRANSACTIONS on Communications
SP - 1085
EP - 1093
AU - Tao LIANG
AU - Flavia GRASSI
AU - Giordano SPADACINI
AU - Sergio Amedeo PIGNARI
PY - 2018
DO - 10.1587/transcom.2017EBP3140
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E101-B
IS - 4
JA - IEICE TRANSACTIONS on Communications
Y1 - April 2018
AB - This work presents a hybrid formulation of the stochastic reduced order model (SROM) algorithm, which makes use of Gauss quadrature, a key ingredient of the stochastic collocation method, to avoid the cumbersome optimization process required by SROM for optimal extraction of the sample set. With respect to classic SROM algorithms, the proposed formulation allows a significant reduction in computation time and burden as well as a remarkable improvement in the accuracy and convergence rate in the estimation of statistical moments. The method is here applied to a specific case study, that is the prediction of crosstalk in a two-conductor wiring structure with electrical and geometrical parameters not perfectly known. Both univariate and multivariate analyses are carried out, with the final objective being to compare the performance of the two SROM formulations with respected to Monte Carlo simulations.
ER -