Details
Originalsprache | Englisch |
---|---|
Aufsatznummer | 111236 |
Seitenumfang | 10 |
Fachzeitschrift | Reliability Engineering and System Safety |
Jahrgang | 263 |
Frühes Online-Datum | 26 Mai 2025 |
Publikationsstatus | Elektronisch veröffentlicht (E-Pub) - 26 Mai 2025 |
Abstract
This contribution presents a novel approach for estimating the Sobol’ index, which has been commonly employed in variance-based sensitivity analysis of computational models that may often involve multiple uncertain parameters. Specifically, a single-loop Monte Carlo simulation (MCS) method, which is independent of the dimensionality of inputs, is proposed to reduce the computational cost of complicated models. The proposed method is realized by developing a new estimator of the Sobol’ index computed via the two-dimensional kernel density estimation, which can be easy programming while ensuring high accuracy. Numerical examples are studied to demonstrate the advantages of the proposed method.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Sicherheit, Risiko, Zuverlässigkeit und Qualität
- Ingenieurwesen (insg.)
- Wirtschaftsingenieurwesen und Fertigungstechnik
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in: Reliability Engineering and System Safety, Jahrgang 263, 111236, 11.2025.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Dimension-independent single-loop Monte Carlo simulation method for estimate of Sobol’ indices in variance-based sensitivity analysis
AU - Wan, Zhiqiang
AU - Wang, Silong
AU - Wu, Ziyan
AU - Wang, Xiuli
N1 - Publisher Copyright: © 2025 Elsevier Ltd
PY - 2025/5/26
Y1 - 2025/5/26
N2 - This contribution presents a novel approach for estimating the Sobol’ index, which has been commonly employed in variance-based sensitivity analysis of computational models that may often involve multiple uncertain parameters. Specifically, a single-loop Monte Carlo simulation (MCS) method, which is independent of the dimensionality of inputs, is proposed to reduce the computational cost of complicated models. The proposed method is realized by developing a new estimator of the Sobol’ index computed via the two-dimensional kernel density estimation, which can be easy programming while ensuring high accuracy. Numerical examples are studied to demonstrate the advantages of the proposed method.
AB - This contribution presents a novel approach for estimating the Sobol’ index, which has been commonly employed in variance-based sensitivity analysis of computational models that may often involve multiple uncertain parameters. Specifically, a single-loop Monte Carlo simulation (MCS) method, which is independent of the dimensionality of inputs, is proposed to reduce the computational cost of complicated models. The proposed method is realized by developing a new estimator of the Sobol’ index computed via the two-dimensional kernel density estimation, which can be easy programming while ensuring high accuracy. Numerical examples are studied to demonstrate the advantages of the proposed method.
KW - Kernel density estimation
KW - Monte Carlo simulation
KW - Uncertainty quantification
KW - Variance-based sensitivity analysis
UR - http://www.scopus.com/inward/record.url?scp=105005937919&partnerID=8YFLogxK
U2 - 10.1016/j.ress.2025.111236
DO - 10.1016/j.ress.2025.111236
M3 - Article
AN - SCOPUS:105005937919
VL - 263
JO - Reliability Engineering and System Safety
JF - Reliability Engineering and System Safety
SN - 0951-8320
M1 - 111236
ER -