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020 ▼a 9780438098268
035 ▼a (MiAaPQ)AAI10901886
035 ▼a (MiAaPQ)OhioLINK:osu1511967797285962
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 247004
0820 ▼a 621
1001 ▼a Yang, Chao.
24510 ▼a On Particle Methods for Uncertainty Quantification in Complex Systems.
260 ▼a [S.l.]: ▼b The Ohio State University., ▼c 2017.
260 1 ▼a Ann Arbor: ▼b ProQuest Dissertations & Theses, ▼c 2017.
300 ▼a 221 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
500 ▼a Adviser: Mrinal Kumar.
5021 ▼a Thesis (Ph.D.)--The Ohio State University, 2017.
520 ▼a This dissertation aims to study three crucial problems related to Monte Carlo based particle methods for solving uncertainty quantification problems in complex systems. The first problem concerns the existence of a "benchmark" sampling method th
520 ▼a Inspired by the new MCMC-MOC approach, a second problem on the transient effectiveness of MCS is posed in the context of Markov chain Monte Carlo theory. The propagated ensemble is viewed as the realization of a Markov chain at each time instant
520 ▼a The third and final problem addressed in this dissertation is the following: "is it possible to develop adaptation rules for MCS such that it may perform within prescribed bounds of accuracy using the "minimum" possible number of simulations at
590 ▼a School code: 0168.
650 4 ▼a Mechanical engineering.
690 ▼a 0548
71020 ▼a The Ohio State University. ▼b Mechanical Engineering.
7730 ▼t Dissertation Abstracts International ▼g 79-12B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0168
791 ▼a Ph.D.
792 ▼a 2017
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T15000309 ▼n KERIS ▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다.
980 ▼a 201812 ▼f 2019
990 ▼a ***1012033