A software framework for probabilistic sensitivity analysis for computationally expensive models

N. Vu-Bac, T. Lahmer, X. Zhuang, T. Nguyen-Thoi, Timon Rabczuk

Research output: Contribution to journalArticle

276 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)19-31
Number of pages13
JournalAdvances in Engineering Software
Volume100
DOIs
Publication statusPublished - 2016 Oct 1
Externally publishedYes

Keywords

  • Matlab toolbox
  • Penalized spline regression
  • Random sampling
  • Sensitivity analysis
  • Uncertainty quantification

ASJC Scopus subject areas

  • Software
  • Engineering(all)

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