Show simple item record

dc.contributor.authorYücel, Abdulkadir C.
dc.contributor.authorBagci, Hakan
dc.contributor.authorMichielssen, Eric
dc.date.accessioned2015-08-03T11:36:30Z
dc.date.available2015-08-03T11:36:30Z
dc.date.issued2013-12
dc.identifier.issn00189375
dc.identifier.doi10.1109/TEMC.2013.2265047
dc.identifier.urihttp://hdl.handle.net/10754/563131
dc.description.abstractAn adaptive multi-element probabilistic collocation (ME-PC) method for quantifying uncertainties in electromagnetic compatibility and interference phenomena involving electrically large, multi-scale, and complex platforms is presented. The method permits the efficient and accurate statistical characterization of observables (i.e., quantities of interest such as coupled voltages) that potentially vary rapidly and/or are discontinuous in the random variables (i.e., parameters that characterize uncertainty in a system's geometry, configuration, or excitation). The method achieves its efficiency and accuracy by recursively and adaptively dividing the domain of the random variables into subdomains using as a guide the decay rate of relative error in a polynomial chaos expansion of the observables. While constructing local polynomial expansions on each subdomain, a fast integral-equation-based deterministic field-cable-circuit simulator is used to compute the observable values at the collocation/integration points determined by the adaptive ME-PC scheme. The adaptive ME-PC scheme requires far fewer (computationally costly) deterministic simulations than traditional polynomial chaos collocation and Monte Carlo methods for computing averages, standard deviations, and probability density functions of rapidly varying observables. The efficiency and accuracy of the method are demonstrated via its applications to the statistical characterization of voltages in shielded/unshielded microwave amplifiers and magnetic fields induced on car tire pressure sensors. © 2013 IEEE.
dc.description.sponsorshipThis work was supported by the National Science Foundation under Grant DMS 0713771, AFOSR/NSSEFF Program Award FA9550-10-1-0180, Sandia Grant "Development of Calderon Multiplicative Preconditioners with Method of Moments Algorithms," KAUST Grant 399813, ONR BRC Grant "Randomized Algorithms for Reduced Representations," and Center for Uncertainty Quantification in Computational Science and Engineering at KAUST.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectAdaptive algorithm
dc.subjectElectromagnetic compatibility and interference (EMC/EMI)
dc.subjectGeneralized polynomial chaos (gPC)
dc.subjectMulti-dimensional integral
dc.subjectMulti-element (ME)
dc.subjectProbabilistic collocation (PC)
dc.subjectSparse grid (SG)
dc.subjectTensor product (TP)
dc.subjectTolerance analysis
dc.subjectUncertainty quantification
dc.titleAn adaptive multi-element probabilistic collocation method for statistical EMC/EMI characterization
dc.typeArticle
dc.contributor.departmentComputational Electromagnetics Laboratory
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalIEEE Transactions on Electromagnetic Compatibility
dc.contributor.institutionDepartment of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, United States
kaust.personBagci, Hakan


This item appears in the following Collection(s)

Show simple item record