TY - JOUR
T1 - A software framework for probabilistic sensitivity analysis for computationally expensive models
AU - Vu-Bac, N.
AU - Lahmer, T.
AU - Zhuang, X.
AU - Nguyen-Thoi, T.
AU - Rabczuk, T.
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.
PY - 2016/10/1
Y1 - 2016/10/1
N2 - 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.
AB - 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.
KW - Matlab toolbox
KW - Penalized spline regression
KW - Random sampling
KW - Sensitivity analysis
KW - Uncertainty quantification
UR - http://www.scopus.com/inward/record.url?scp=84975855891&partnerID=8YFLogxK
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U2 - 10.1016/j.advengsoft.2016.06.005
DO - 10.1016/j.advengsoft.2016.06.005
M3 - Article
AN - SCOPUS:84975855891
VL - 100
SP - 19
EP - 31
JO - Advances in Engineering Software
JF - Advances in Engineering Software
SN - 0965-9978
ER -