A Computational Method for Sensitivity Analysis under Uncertainty

DOI

10.2991/lemcs-15.2015.63How to use a DOI?

Keywords

Generalized Polynomial Chaos; Sensitivity Analysis; Stochastic Collocation; Sensitivity Coefficient; Uncertainty

Abstract

Sensitivity analysis (SA) is an important part in engineering design under the uncertainty to provide valuable information about the probabilistic characteristics of a response. In this paper, the variance-based methods and the cumulative distribution function (CDF)-based sensitivity coefficients were used in sensitivity analysis. The combination of sparse grid stochastic collocation (SC) and the generalized polynomial chaos (gPC) are proposed as a method to perform the sensitivity analysis. The computational method employs the gPC as a high-order representation for random quantities, a stochastic collocation (SC) approach to deal with complex/implicit response functions, and sparse grid to use a reduced set of samples. It can reduce the computational cost associated with uncertainty assessment without much sacrifice on the optimum solution. The effectiveness is demonstrated in two numerical examples.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Hongchun Wang
PY  - 2015/07
DA  - 2015/07
TI  - A Computational Method for Sensitivity Analysis under Uncertainty
BT  - Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science
PB  - Atlantis Press
SP  - 326
EP  - 330
SN  - 1951-6851
UR  - https://doi.org/10.2991/lemcs-15.2015.63
DO  - 10.2991/lemcs-15.2015.63
ID  - Wang2015/07
ER  -