Imperial College London
Alternate

Professor Nilay Shah OBE FREng

Faculty of Engineering, Department of Chemical Engineering

Professor of Process Systems Engineering
 
 
 
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Contact

 

+44 (0)20 7594 6621n.shah

 
 
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Assistant

 

Miss Jessica Baldock +44 (0)20 7594 5699

 
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Location

 

ACEX 522ACE ExtensionSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Lambert:2016:10.1016/j.matcom.2016.04.005,
author = {Lambert, R and Lemke, F and Song, S and Kucherenko, S and Shah, N},
doi = {10.1016/j.matcom.2016.04.005},
journal = {Mathematics and Computers in Simulation},
pages = {42--54},
title = {Global sensitivity analysis using sparse high dimensional model representations generated by the group method of data handling},
url = {http://dx.doi.org/10.1016/j.matcom.2016.04.005},
volume = {128},
year = {2016}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Lambert,R
AU  - Lemke,F
AU  - Song,S
AU  - Kucherenko,S
AU  - Shah,N
DO  - 10.1016/j.matcom.2016.04.005
EP  - 54
PY  - 2016///
SN  - 1872-7166
SP  - 42
TI  - Global sensitivity analysis using sparse high dimensional model representations generated by the group method of data handling
T2  - Mathematics and Computers in Simulation
UR  - http://dx.doi.org/10.1016/j.matcom.2016.04.005
VL  - 128
ER  -
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