QA222 : Sensitivity analysis of computer models using Bayesian surrogate model
Thesis > Central Library of Shahrood University > Mathematical Sciences > MSc > 2013
Authors:
Abstarct: Computer models contain many inputs, the real value of their are uncertain.
In current methods of sensitivity analysis for assessing the effects of the input
uncertainties on the output of the computer models are used from Monte Carlo
methods. For computer model with complex and expensive computational, it
is often not possible to get a large enough sample for a meaningful uncertainty
analysis. In this thesis, we are doing sensitivity analysis with presentation kriging
model as a surrogate model, that is estimator of computer model, baxsed on the use
of gaussian stochastic process models, in a Bayesian context. All of the results and
measures provided by the Monte Carlo sensitivity analysis of the computer model
are obtained with surrogate model, but using a far smaller sample. The accuracy
of the kriging model is shown with an analytical function and a case study.
Keywords:
#Computer models #Sensitivity analysis #Surrogate model #Gaussian stochastic #Kriging #Bayesian approach Link
Keeping place: Central Library of Shahrood University
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Keeping place: Central Library of Shahrood University
Visitor: