期刊
JOURNAL OF COMPUTATIONAL PHYSICS
卷 229, 期 23, 页码 8966-8980出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jcp.2010.08.022
关键词
Failure probability; Sampling; Polynomial chaos; Stochastic computation
资金
- AFOSR, DOE/NNSA, NSF
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [0914564] Funding Source: National Science Foundation
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [0914447] Funding Source: National Science Foundation
Evaluation of failure probability of a given system requires sampling of the system response and can be computationally expensive. Therefore it is desirable to construct an accurate surrogate model for the system response and subsequently to sample the surrogate model. In this paper we discuss the properties of this approach. We demonstrate that the straightforward sampling of a surrogate model can lead to erroneous results, no matter how accurate the surrogate model is. We then propose a hybrid approach by sampling both the surrogate model in a large portion of the probability space and the original system in a small portion. The resulting algorithm is significantly more efficient than the traditional sampling method, and is more accurate and robust than the straightforward surrogate model approach. Rigorous convergence proof is established for the hybrid approach, and practical implementation is discussed. Numerical examples are provided to verify the theoretical findings and demonstrate the efficiency gain of the approach. (C) 2010 Elsevier Inc. All rights reserved.
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