文章基本信息

标题：Computer experiments with functional outputs: Global sensitivity analysis and metamodeling
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作者：Benjamin Auder ; Benjamin Auder ; Bertrand Iooss 等
期刊名称：Procedia - Social and Behavioral Sciences
印刷版ISSN：1877-0428
出版年度：2010
卷号：2
期号：6
页码：7583-7584
DOI：10.1016/j.sbspro.2010.05.129
语种：English
出版社：Elsevier
摘要：AbstractTo perform uncertainty, sensitivity or optimization analysis on scalar variables calculated by a cpu time expensive computer code, a widely accepted methodology consists in first identifying the most influential uncertain inputs (by screening techniques), and then in replacing the cpu time expensive model by a cpu inexpensive mathematical function, called a metamodel. This paper extends this methodology to the functional output case, for instance when the model output variables are curves. Our screening approach is based on the analysis of variance and principal component analysis of output curves. Our functional metamodeling consists in a curve classification step, a dimension reduction step, then a classical metamodeling step. An industrial nuclear reactor application (dealing with uncertainties in the pressurized thermal shock analysis) illustrates all these steps.
关键词：Computer model;Curve classification;Functional data;Metamodel;sensitivity indices;Uncertainty analysis