Journal
ADVANCES IN ENGINEERING SOFTWARE
Volume 100, Issue -, Pages 19-31Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.advengsoft.2016.06.005
Keywords
Uncertainty quantification; Random sampling; Penalized spline regression; Sensitivity analysis; Matlab toolbox
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Funding
- National Basic Research Program of China (973 Program) [2011CB013800]
- NSFC [41130751]
- Ministry of Science and Technology of China [SLDRCE14-B-31]
- Science and Technology Commission of Shanghai Municipality [16QA1404000]
- IRSES-MULTIFRAC
- Alexander von Humboldt Foundation
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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. (C) 2016 Elsevier Ltd. All rights reserved.
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