Novel methods to quantify aleatory and epistemic uncertainty in high speed networks
| dc.contributor.author | Kapse, Ishan Deepak, author | |
| dc.contributor.author | Roy, Sourajeet, advisor | |
| dc.contributor.author | Pasricha, Sudeep, committee member | |
| dc.contributor.author | Anderson, Charles, committee member | |
| dc.date.accessioned | 2017-09-14T16:07:09Z | |
| dc.date.available | 2017-09-14T16:07:09Z | |
| dc.date.issued | 2017 | |
| dc.description.abstract | With the sustained miniaturization of integrated circuits to sub-45 nm regime and the increasing packaging density, random process variations have been found to result in unpredictability in circuit performance. In existing literature, this unpredictability has been modeled by creating polynomial expansions of random variables. But the existing methods prove inefficient because as the number of random variables within a system increase, the time and computational cost increases in a near-polynomial fashion. In order to mitigate this poor scalability of conventional approaches, several techniques are presented, in this dissertation, to sparsify the polynomial expansion. The sparser polynomial expansion is created, by identifying the contribution of each random variable on the total response of the system. This sparsification is performed primarily using two different methods. It translates to immense savings, in the time required, and the memory cost of computing the expansion. One of the two methods presented is applied to aleatory variability problems while the second method is applied to problems involving epistemic uncertainty. The accuracy of the proposed approaches is validated through multiple numerical examples. | |
| dc.format.medium | born digital | |
| dc.format.medium | masters theses | |
| dc.identifier | Kapse_colostate_0053N_14436.pdf | |
| dc.identifier.uri | https://hdl.handle.net/10217/184055 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2000-2019 | |
| dc.rights | Copyright 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.subject | epistemic uncertainty | |
| dc.subject | uncertainty quantification | |
| dc.subject | fuzzy sets | |
| dc.subject | aleatory uncertainty | |
| dc.title | Novel methods to quantify aleatory and epistemic uncertainty in high speed networks | |
| dc.type | Text | |
| dcterms.rights.dpla | This 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.discipline | Electrical and Computer Engineering | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science (M.S.) |
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