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A Surrogate-based Variance Reduction Approach to Multifidelity Uncertainty Quantification - with Applications in Automotive Systems
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | A Surrogate-based Variance Reduction Approach to Multifidelity Uncertainty Quantification - with Applications in Automotive Systems. |
| 개인저자 | Yang, Hang. |
| 단체저자명 | University of Michigan. Mechanical Engineering. |
| 발행사항 | [S.l.]: University of Michigan., 2022. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2022. |
| 형태사항 | 143 p. |
| 기본자료 저록 | Dissertations Abstracts International 84-01B. Dissertation Abstract International |
| ISBN | 9798438777380 |
| 학위논문주기 | Thesis (Ph.D.)--University of Michigan, 2022. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 84-01, Section: B.
Advisor: Gorodetsky, Alex;Wang, Kon-Well. |
| 이용제한사항 | This item must not be sold to any third party vendors.This item must not be added to any third party search indexes. |
| 일반주제명 | Mathematics. Mechanics. Mechanical engineering. Applied mathematics. Computer science. Automotive engineering. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
