State dependent parameter metamodelling and sensitivity analysis
Date
2007
Authors
Ratto, Marco
Pagano, Andrea
Young, Peter C
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Elsevier
Abstract
In this paper we propose a general framework to deal with model approximation and analysis. We present a unified procedure which exploits sampling, screening and model approximation techniques in order to optimally fulfill basic requirements in terms of general applicability and flexibility, efficiency of estimation and simplicity of implementation. The sampling procedure applies Sobol' quasi-Monte Carlo sequences, which display optimal characteristics when linked to a screening procedure, such as the elementary effect test. The latter method is used to reduce the dimensionality of the problem and allows for a preliminary sorting of the factors in terms of their relative importance. Then we apply State Dependent Parameter (SDP) modelling (a model estimation approach, based on recursive filtering and smoothing estimation) to build an approximation of the computational model under analysis and to estimate the variance based sensitivity indices. The method is conceptually simple and very efficient, leading to a significant reduction in the cost of the analysis. All measures of interest are computed using a single set of quasi-Monte Carlo runs. The approach is flexible because, in principle, it can be applied with any available type of Monte Carlo sample.
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Keywords: Computational methods; Cost reduction; Mathematical models; Monte Carlo methods; Parameter estimation; Sensitivity analysis; High dimensional model representations; Metamodellings; Sensitivity indices; State Dependent Parameter models; Metadata High dimensional model representation; Metamodelling; Sensitivity analysis; State Dependent Parameter models
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Computer Physics Communications
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Journal article
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2037-12-31
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