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Low-Rank, Multi-Fidelity Methods for Uncertainty Quantification of High-Dimensional Systems
Low-Rank, Multi-Fidelity Methods for Uncertainty Quantification of High-Dimensional Systems
상세정보
- 자료유형
- 학위논문
- Control Number
- 0014997705
- International Standard Book Number
- 9780355966053
- Dewey Decimal Classification Number
- 519
- Main Entry-Personal Name
- Fairbanks, H. R.
- Publication, Distribution, etc. (Imprint
- [Sl] : University of Colorado at Boulder, 2018
- Publication, Distribution, etc. (Imprint
- Ann Arbor : ProQuest Dissertations & Theses, 2018
- Physical Description
- 194 p
- General Note
- Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
- General Note
- Advisers: Alireza Doostan
- Dissertation Note
- Thesis (Ph.D.)--University of Colorado at Boulder, 2018.
- Summary, Etc.
- 요약Characterizing and incorporating uncertainties when simulating physical phenomena is essential for improving model-based predictions. These uncertainties may stem from a lack of knowledge regarding the underlying physical processes or from impre
- Summary, Etc.
- 요약For systems exhibiting high-dimensional uncertainty, performing either forward or inverse UQ presents a significant computational challenge, as these methods require a large number forward solves of the high-fidelity model, that is, the model th
- Summary, Etc.
- 요약To reduce the cost of performing UQ on high-dimensional systems, we apply multi-fidelity strategies to both the forward problem, in order to estimate moments of the quantity of interest, and inverse problem, to approximate the posterior covarian
- Subject Added Entry-Topical Term
- Applied mathematics
- Added Entry-Corporate Name
- University of Colorado at Boulder Applied Mathematics
- Host Item Entry
- Dissertation Abstracts International. 79-10B(E).
- Host Item Entry
- Dissertation Abstract International
- Electronic Location and Access
- 로그인을 한후 보실 수 있는 자료입니다.
- Control Number
- joongbu:556172
MARC
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■035 ▼a(MiAaPQ)AAI10792380
■035 ▼a(MiAaPQ)colorado:15424
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a519
■1001 ▼aFairbanks, H. R.
■24510▼aLow-Rank, Multi-Fidelity Methods for Uncertainty Quantification of High-Dimensional Systems
■260 ▼a[Sl]▼bUniversity of Colorado at Boulder▼c2018
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2018
■300 ▼a194 p
■500 ▼aSource: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
■500 ▼aAdvisers: Alireza Doostan
■5021 ▼aThesis (Ph.D.)--University of Colorado at Boulder, 2018.
■520 ▼aCharacterizing and incorporating uncertainties when simulating physical phenomena is essential for improving model-based predictions. These uncertainties may stem from a lack of knowledge regarding the underlying physical processes or from impre
■520 ▼aFor systems exhibiting high-dimensional uncertainty, performing either forward or inverse UQ presents a significant computational challenge, as these methods require a large number forward solves of the high-fidelity model, that is, the model th
■520 ▼aTo reduce the cost of performing UQ on high-dimensional systems, we apply multi-fidelity strategies to both the forward problem, in order to estimate moments of the quantity of interest, and inverse problem, to approximate the posterior covarian
■590 ▼aSchool code: 0051.
■650 4▼aApplied mathematics
■690 ▼a0364
■71020▼aUniversity of Colorado at Boulder▼bApplied Mathematics.
■7730 ▼tDissertation Abstracts International▼g79-10B(E).
■773 ▼tDissertation Abstract International
■790 ▼a0051
■791 ▼aPh.D.
■792 ▼a2018
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T14997705▼nKERIS
■980 ▼a201812▼f2019
