Efficient stochastic EMC/EMI analysis using HDMR-generated surrogate models

Abdulkadir C. Yücel, Hakan Bagci, Eric Michielssen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations


Stochastic methods have been used extensively to quantify effects due to uncertainty in system parameters (e.g. material, geometrical, and electrical constants) and/or excitation on observables pertinent to electromagnetic compatibility and interference (EMC/EMI) analysis (e.g. voltages across mission-critical circuit elements) [1]. In recent years, stochastic collocation (SC) methods, especially those leveraging generalized polynomial chaos (gPC) expansions, have received significant attention [2, 3]. SC-gPC methods probe surrogate models (i.e. compact polynomial input-output representations) to statistically characterize observables. They are nonintrusive, that is they use existing deterministic simulators, and often cost only a fraction of direct Monte-Carlo (MC) methods. Unfortunately, SC-gPC-generated surrogate models often lack accuracy (i) when the number of uncertain/random system variables is large and/or (ii) when the observables exhibit rapid variations. © 2011 IEEE.
Original languageEnglish (US)
Title of host publication2011 XXXth URSI General Assembly and Scientific Symposium
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Print)9781424451173
StatePublished - Aug 2011

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01


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