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Accurate dimension reduction based polynomial chaos approach for uncertainty quantification of high speed networks

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dc.contributor.authorKrishna Prasad, Aditi, author
dc.contributor.authorRoy, Sourajeey, advisor
dc.contributor.authorPezeshki, Ali, committee member
dc.contributor.authorNotaros, Branislav, committee member
dc.contributor.authorAnderson, Charles, committee member
dc.date.accessioned2018-06-12T16:14:17Z
dc.date.available2018-06-12T16:14:17Z
dc.date.issued2018
dc.description.abstractWith the continued miniaturization of VLSI technology to sub-45 nm levels, uncertainty in nanoscale manufacturing processes and operating conditions have been found to translate into unpredictable system-level behavior of integrated circuits. As a result, there is a need for contemporary circuit simulation tools/solvers to model the forward propagation of device level uncertainty to the network response. Recently, techniques based on the robust generalized polynomial chaos (PC) theory have been reported for the uncertainty quantification of high-speed circuit, electromagnetic, and electronic packaging problems. The major bottleneck in all PC approaches is that the computational effort required to generate the metamodel scales in a polynomial fashion with the number of random input dimensions. In order to mitigate this poor scalability of conventional PC approaches, in this dissertation, a reduced dimensional PC approach is proposed. This PC approach is based on using a high dimensional model representation (HDMR) to quantify the relative impact of each dimension on the variance of the network response. The reduced dimensional PC approach is further extended to problems with mixed aleatory and epistemic uncertainties. In this mixed PC approach, a parameterized formulation of analysis of variance (ANOVA) is used to identify the statistically significant dimensions and subsequently perform dimension reduction. Mixed problems are however characterized by far greater number of dimensions than purely epistemic or aleatory problems, thus exacerbating the poor scalability of PC expansions. To address this issue, in this dissertation, a novel dimension fusion approach is proposed. This approach fuses the epistemic and aleatory dimensions within the same model parameter into a mixed dimension. The accuracy and efficiency of the proposed approaches are validated through multiple numerical examples.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierKrishnaPrasad_colostate_0053A_14773.pdf
dc.identifier.urihttps://hdl.handle.net/10217/189391
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectdimension fusion
dc.subjectpolynomial chaos
dc.subjectuncertainty quantification
dc.subjectepistemic
dc.subjectaleatory
dc.subjectsensitivity indices
dc.titleAccurate dimension reduction based polynomial chaos approach for uncertainty quantification of high speed networks
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

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