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dc.contributor.authorBai, Jinjunen
dc.contributor.authorZhang, Gangen
dc.contributor.authorWang, Dien
dc.contributor.authorDuffy, A. P.en
dc.contributor.authorWang, Lixinen
dc.date.accessioned2016-08-15T10:08:49Z
dc.date.available2016-08-15T10:08:49Z
dc.date.issued2016-09-23
dc.identifier.citationBai, J. et al. (2016) Performance Comparison of the SGM and the SCM in EMC Simulation. IEEET Transactions on EMC, 2016, 58 (6), pp. 1739-1746en
dc.identifier.urihttp://hdl.handle.net/2086/12454
dc.description.abstractUncertainty analysis methods are widely used in today’s Electromagnetic Compatibility (EMC) simulations in order to take account of the non-ideality and unpredictability in reality and improve the reliability of simulation results. The Stochastic Galerkin Method (SGM) and the Stochastic Collocation Method (SCM), both based on the generalized Polynomial Chaos (gPC) expansion theory, have become two prevailing types of uncertainty analysis methods thanks to their high accuracy and high computational efficiency. This paper, by using the Feature Selective Validation (FSV) method, presents the quantitative accuracy comparison between the foregoing two methods, with the commonly used Monte Carlo Method (MCM) used as the comparison reference. This paper also introduces SCM into the CST software simulation as an example of performing uncertainty analysis. The advantages and limitations of SGM and SCM are discussed in detail in this paper. Finally, the strategy of how to choose between SGM, SCM, and MCM under different situations is proposed in the conclusion section.en
dc.language.isoenen
dc.publisherIEEEen
dc.subjectStochastic Galerkin Methoden
dc.subjectStochastic Collocation Methoden
dc.subjectUncertainty Analysisen
dc.subjectElectromagnetic Compatibility Simulationen
dc.titlePerformance Comparison of the SGM and the SCM in EMC Simulationen
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.1109/temc.2016.2588580
dc.researchgroupCentre for Electronic and Communications Engineeringen
dc.peerreviewedYesen
dc.funderThis work was supported by the National Natural Science Foundational of China under Grant 51477036en
dc.projectidGrant number 51477036en
dc.cclicenceCC-BY-NCen
dc.date.acceptance2016-06-29en
dc.researchinstituteInstitute of Engineering Sciences (IES)en


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