A software framework for probabilistic sensitivity analysis for computationally expensive models

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Autoren

  • Nam Vu-Bac
  • T. Lahmer
  • Xiaoying Zhuang
  • T. Nguyen-Thoi
  • Timon Rabczuk

Organisationseinheiten

Externe Organisationen

  • Bauhaus-Universität Weimar
  • Tongji University
  • Ton Duc Thang University
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Seiten (von - bis)19-31
Seitenumfang13
FachzeitschriftAdvances in Engineering Software
Jahrgang100
PublikationsstatusVeröffentlicht - 22 Juni 2016

Abstract

We provide a sensitivity analysis toolbox consisting of a set of Matlab functions that offer utilities for quantifying the influence of uncertain input parameters on uncertain model outputs. It allows the determination of the key input parameters of an output of interest. The results are based on a probability density function (PDF) provided for the input parameters. The toolbox for uncertainty and sensitivity analysis methods consists of three ingredients: (1) sampling method, (2) surrogate models, (3) sensitivity analysis (SA) method. Numerical studies based on analytical functions associated with noise and industrial data are performed to prove the usefulness and effectiveness of this study.

ASJC Scopus Sachgebiete

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A software framework for probabilistic sensitivity analysis for computationally expensive models. / Vu-Bac, Nam; Lahmer, T.; Zhuang, Xiaoying et al.
in: Advances in Engineering Software, Jahrgang 100, 22.06.2016, S. 19-31.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Vu-Bac N, Lahmer T, Zhuang X, Nguyen-Thoi T, Rabczuk T. A software framework for probabilistic sensitivity analysis for computationally expensive models. Advances in Engineering Software. 2016 Jun 22;100:19-31. doi: 10.1016/j.advengsoft.2016.06.005
Vu-Bac, Nam ; Lahmer, T. ; Zhuang, Xiaoying et al. / A software framework for probabilistic sensitivity analysis for computationally expensive models. in: Advances in Engineering Software. 2016 ; Jahrgang 100. S. 19-31.
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abstract = "We provide a sensitivity analysis toolbox consisting of a set of Matlab functions that offer utilities for quantifying the influence of uncertain input parameters on uncertain model outputs. It allows the determination of the key input parameters of an output of interest. The results are based on a probability density function (PDF) provided for the input parameters. The toolbox for uncertainty and sensitivity analysis methods consists of three ingredients: (1) sampling method, (2) surrogate models, (3) sensitivity analysis (SA) method. Numerical studies based on analytical functions associated with noise and industrial data are performed to prove the usefulness and effectiveness of this study.",
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AU - Nguyen-Thoi, T.

AU - Rabczuk, Timon

N1 - Funding information: We gratefully acknowledge the support from National Basic Research Program of China (973 Program: 2011CB013800), NSFC (41130751), the Ministry of Science and Technology of China (SLDRCE14-B-31), Science and Technology Commission of Shanghai Municipality (16QA1404000), IRSES-MULTIFRAC. The support from the Alexander von Humboldt Foundation in the framework of the Sofja Kovalevskaja Award endowed by the Federal Ministry of Education and Research is acknowledged by X. Zhuang.

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