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Dimension-independent single-loop Monte Carlo simulation method for estimate of Sobol’ indices in variance-based sensitivity analysis

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Autorschaft

  • Zhiqiang Wan
  • Silong Wang
  • Ziyan Wu
  • Xiuli Wang

Organisationseinheiten

Externe Organisationen

  • Northwestern Polytechnical University
  • Forschungszentrum Jülich

Details

OriginalspracheEnglisch
Aufsatznummer111236
Seitenumfang10
FachzeitschriftReliability Engineering and System Safety
Jahrgang263
Frühes Online-Datum26 Mai 2025
PublikationsstatusElektronisch 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

Zitieren

Dimension-independent single-loop Monte Carlo simulation method for estimate of Sobol’ indices in variance-based sensitivity analysis. / Wan, Zhiqiang; Wang, Silong; Wu, Ziyan et al.
in: Reliability Engineering and System Safety, Jahrgang 263, 111236, 11.2025.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

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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

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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

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