| LDR | | 02096nam u200397 4500 |
| 001 | | 000000418895 |
| 005 | | 20190215163235 |
| 008 | | 181129s2017 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438098268 |
| 035 | |
▼a (MiAaPQ)AAI10901886 |
| 035 | |
▼a (MiAaPQ)OhioLINK:osu1511967797285962 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 621 |
| 100 | 1 |
▼a Yang, Chao. |
| 245 | 10 |
▼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. |
| 502 | 1 |
▼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 |
| 710 | 20 |
▼a The Ohio State University.
▼b Mechanical Engineering. |
| 773 | 0 |
▼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 |
| 856 | 40 |
▼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 |