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dc.contributor.authorEl-Moselhy, Tarek A.
dc.contributor.authorElfadel, Ibrahim M.
dc.contributor.authorZhang, Zheng
dc.contributor.authorDaniel, Luca
dc.date.accessioned2017-04-19T19:16:12Z
dc.date.available2017-04-19T19:16:12Z
dc.date.issued2014-04
dc.identifier.issn0278-0070
dc.identifier.issn1937-4151
dc.identifier.urihttp://hdl.handle.net/1721.1/108269
dc.description.abstractStochastic spectral methods are efficient techniques for uncertainty quantification. Recently they have shown excellent performance in the statistical analysis of integrated circuits. In stochastic spectral methods, one needs to determine a set of orthonormal polynomials and a proper numerical quadrature rule. The former are used as the basis functions in a generalized polynomial chaos expansion. The latter is used to compute the integrals involved in stochastic spectral methods. Obtaining such information requires knowing the density function of the random input a-priori. However, individual system components are often described by surrogate models rather than density functions. In order to apply stochastic spectral methods in hierarchical uncertainty quantification, we first propose to construct physically consistent closed-form density functions by two monotone interpolation schemes. Then, by exploiting the special forms of the obtained density functions, we determine the generalized polynomial-chaos basis functions and the Gauss quadrature rules that are required by a stochastic spectral simulator. The effectiveness of our proposed algorithm is verified by both synthetic and practical circuit examples.en_US
dc.description.sponsorshipMassachusetts Institute of Technology-Skolkovo Institute of Science and Technology programen_US
dc.description.sponsorshipMIT & Masdar Institute Cooperative Programen_US
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/TCAD.2013.2295818en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceProf. Daniel via Phoebe Ayersen_US
dc.titleCalculation of Generalized Polynomial-Chaos Basis Functions and Gauss Quadrature Rules in Hierarchical Uncertainty Quantificationen_US
dc.title.alternativeCalculation of Generalized Polynomial-Chaos Basis Functions and Gauss Quadrature Rules in Hierarchical Uncertainty Quantificationen_US
dc.typeArticleen_US
dc.identifier.citationZheng Zhang; El-Moselhy, Tarek A.; Elfadel, Ibrahim M. and Daniel, Luca. "Calculation of Generalized Polynomial-Chaos Basis Functions and Gauss Quadrature Rules in Hierarchical Uncertainty Quantification." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 33, no. 5 (May 2014): 728-740. © 2013 Institute of Electrical and Electronics Engineers (IEEE)en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.departmentMassachusetts Institute of Technology. Research Laboratory of Electronicsen_US
dc.contributor.approverDaniel, Lucaen_US
dc.contributor.mitauthorZhang, Zheng
dc.contributor.mitauthorDaniel, Luca
dc.relation.journalIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systemsen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsZheng Zhang; El-Moselhy, Tarek A.; Elfadel, Ibrahim M.; Daniel, Lucaen_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0002-5880-3151
mit.licenseOPEN_ACCESS_POLICYen_US


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