Details
| Original language | English |
|---|---|
| Article number | 106316 |
| Journal | Mechanical Systems and Signal Processing |
| Volume | 134 |
| Early online date | 27 Aug 2019 |
| Publication status | Published - 1 Dec 2019 |
Abstract
Non-intrusive Imprecise Stochastic Simulation (NISS) is a recently developed general methodological framework for efficiently propagating the imprecise probability models and for estimating the resultant failure probability functions and bounds. Due to the simplicity, high efficiency, stability and good convergence, it has been proved to be one of the most appealing forward uncertainty quantification methods. However, the current version of NISS is only applicable for model with input variables characterized by precise and imprecise probability models. In real-world applications, the uncertainties of model inputs may also be characterized by non-probabilistic models such as interval model due to the extreme scarcity or imprecise information. In this paper, the NISS method is generalized for models with three kinds of mixed inputs characterized by precise probability model, non-probabilistic models and imprecise probability models respectively, and specifically, the interval model and distributional p-box model are exemplified. This generalization is realized by combining Bayes rule and the global NISS method, and is shown to conserve all the advantages of the classical NISS method. With this generalization, the three kinds of inputs can be propagated with only one set of function evaluations in a pure simulation manner, and two kinds of potential estimation errors are properly addressed by sensitivity indices and bootstrap. A numerical test example and the NASA uncertainty quantification challenging problem are solved to demonstrate the effectiveness of the generalized NISS procedure.
Keywords
- Bayes rule, Bootstrap, Imprecise probability, Interval model, Non-intrusive imprecise stochastic simulation, Non-probabilistic, Sensitivity, Uncertainty quantification
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Signal Processing
- Engineering(all)
- Civil and Structural Engineering
- Engineering(all)
- Aerospace Engineering
- Engineering(all)
- Mechanical Engineering
- Computer Science(all)
- Computer Science Applications
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In: Mechanical Systems and Signal Processing, Vol. 134, 106316, 01.12.2019.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Generalization of non-intrusive imprecise stochastic simulation for mixed uncertain variables
AU - Song, Jingwen
AU - Wei, Pengfei
AU - Valdebenito, Marcos
AU - Bi, Sifeng
AU - Broggi, Matteo
AU - Beer, Michael
AU - Lei, Zuxiang
N1 - Funding Information: This work is supported by the National Natural Science of China (NSFC 51905430) and the Aerospace Science and Technology Foundation of China. The first author is supported by the program of China Scholarships Council (CSC). The second to fourth authors are all supported by the Alexander von Humboldt Foundation of Germany. The second author is also supported by the Top International University Visiting Program for Outstanding Young Scholars of Northwestern Polytechnical University. The third author acknowledges the support by CONICYT (National Commission for Scientific and Technological Research) under grant number 1180271. The authors also appreciate the two anonymous reviewers for their very useful comments and suggestions.
PY - 2019/12/1
Y1 - 2019/12/1
N2 - Non-intrusive Imprecise Stochastic Simulation (NISS) is a recently developed general methodological framework for efficiently propagating the imprecise probability models and for estimating the resultant failure probability functions and bounds. Due to the simplicity, high efficiency, stability and good convergence, it has been proved to be one of the most appealing forward uncertainty quantification methods. However, the current version of NISS is only applicable for model with input variables characterized by precise and imprecise probability models. In real-world applications, the uncertainties of model inputs may also be characterized by non-probabilistic models such as interval model due to the extreme scarcity or imprecise information. In this paper, the NISS method is generalized for models with three kinds of mixed inputs characterized by precise probability model, non-probabilistic models and imprecise probability models respectively, and specifically, the interval model and distributional p-box model are exemplified. This generalization is realized by combining Bayes rule and the global NISS method, and is shown to conserve all the advantages of the classical NISS method. With this generalization, the three kinds of inputs can be propagated with only one set of function evaluations in a pure simulation manner, and two kinds of potential estimation errors are properly addressed by sensitivity indices and bootstrap. A numerical test example and the NASA uncertainty quantification challenging problem are solved to demonstrate the effectiveness of the generalized NISS procedure.
AB - Non-intrusive Imprecise Stochastic Simulation (NISS) is a recently developed general methodological framework for efficiently propagating the imprecise probability models and for estimating the resultant failure probability functions and bounds. Due to the simplicity, high efficiency, stability and good convergence, it has been proved to be one of the most appealing forward uncertainty quantification methods. However, the current version of NISS is only applicable for model with input variables characterized by precise and imprecise probability models. In real-world applications, the uncertainties of model inputs may also be characterized by non-probabilistic models such as interval model due to the extreme scarcity or imprecise information. In this paper, the NISS method is generalized for models with three kinds of mixed inputs characterized by precise probability model, non-probabilistic models and imprecise probability models respectively, and specifically, the interval model and distributional p-box model are exemplified. This generalization is realized by combining Bayes rule and the global NISS method, and is shown to conserve all the advantages of the classical NISS method. With this generalization, the three kinds of inputs can be propagated with only one set of function evaluations in a pure simulation manner, and two kinds of potential estimation errors are properly addressed by sensitivity indices and bootstrap. A numerical test example and the NASA uncertainty quantification challenging problem are solved to demonstrate the effectiveness of the generalized NISS procedure.
KW - Bayes rule
KW - Bootstrap
KW - Imprecise probability
KW - Interval model
KW - Non-intrusive imprecise stochastic simulation
KW - Non-probabilistic
KW - Sensitivity
KW - Uncertainty quantification
UR - http://www.scopus.com/inward/record.url?scp=85071320122&partnerID=8YFLogxK
U2 - 10.1016/j.ymssp.2019.106316
DO - 10.1016/j.ymssp.2019.106316
M3 - Article
AN - SCOPUS:85071320122
VL - 134
JO - Mechanical Systems and Signal Processing
JF - Mechanical Systems and Signal Processing
SN - 0888-3270
M1 - 106316
ER -