A software framework for probabilistic sensitivity analysis for computationally expensive models

authored by: Nam Vu-Bac, T. Lahmer, Xiaoying Zhuang, T. Nguyen-Thoi, Timon Rabczuk
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.
Organisation(s): Institute of Continuum Mechanics
External Organisation(s): Bauhaus-Universität Weimar
Tongji University
Ton Duc Thang University
Type: Article
Journal: Advances in Engineering Software
Volume: 100
Pages: 19-31
No. of pages: 13
ISSN: 0965-9978
Publication date: 22.06.2016
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Software, General Engineering
Electronic version(s): https://doi.org/10.1016/j.advengsoft.2016.06.005 (Access: Closed)
: Details in the research portal "Research@Leibniz University"

BibTeX

@article{ca212bc1fbe2405a8acf4842acfc637d,
title = "A software framework for probabilistic sensitivity analysis for computationally expensive models",
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.",
keywords = "Matlab toolbox, Penalized spline regression, Random sampling, Sensitivity analysis, Uncertainty quantification",
author = "Nam Vu-Bac and T. Lahmer and Xiaoying Zhuang and T. Nguyen-Thoi and Timon Rabczuk",
note = "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.",
year = "2016",
month = jun,
day = "22",
doi = "10.1016/j.advengsoft.2016.06.005",
language = "English",
volume = "100",
pages = "19--31",
journal = "Advances in Engineering Software",
issn = "0965-9978",
publisher = "Elsevier Ltd.",
}