Global sensitivity analysis using sparse high dimensional model representations generated by the group method of data handling

Romain S.C. Lambert, Frank Lemke, Sergei S. Kucherenko, Shufang Song and Nilay Shah

Mathematics and Computers in Simulation (MATCOM), 2016, vol. 128, issue C, 42-54

Abstract: In this paper, the parameter selection capabilities of the group method of data handling (GMDH) as an inductive self-organizing modelling method are used to construct sparse random sampling high dimensional model representations (RS-HDMR), from which the Sobol’s first and second order global sensitivity indices can be derived. The proposed method is capable of dealing with high-dimensional problems without the prior use of a screening technique and can perform with a relatively limited number of function evaluations, even in the case of under-determined modelling problems. Four classical benchmark test functions are used for the evaluation of the proposed technique.

Keywords: Global sensitivity analysis; High dimensional model representations; Sobol indices; Group method of data handling (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:128:y:2016:i:c:p:42-54

DOI: 10.1016/j.matcom.2016.04.005

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