Journal article
A surrogate method for density-based global sensitivity analysis
Reliability engineering & system safety, Vol.155, pp.224-235
11/2016
DOI: 10.1016/j.ress.2016.07.002
Abstract
This paper describes an accurate and computationally efficient surrogate method, known as the polynomial dimensional decomposition (PDD) method, for estimating a general class of density-based f-sensitivity indices. Unlike the variance-based Sobol index, the f-sensitivity index is applicable to random input following dependent as well as independent probability distributions. The proposed method involves PDD approximation of a high-dimensional stochastic response of interest, forming a surrogate input–output data set; kernel density estimations of output probability density functions from the surrogate data set; and subsequent Monte Carlo integration for estimating the f-sensitivity index. Developed for an arbitrary convex function f and an arbitrary probability distribution of input variables, the method is capable of calculating a wide variety of sensitivity or importance measures, including the mutual information, squared-loss mutual information, and L1-distance-based importance measure. Three numerical examples illustrate the accuracy, efficiency, and convergence properties of the proposed method in computing sensitivity indices derived from three prominent divergence or distance measures. A finite-element-based global sensitivity analysis of a leverarm was performed, demonstrating the ability of the method in solving industrial-scale engineering problems.
•A novel computational method for calculating f -sensitivity indices is proposed.•Both dependent and independent input random variables are applicable.•The method can calculate a wide variety of importance or sensitivity measures.•The estimator is more efficient than existing methods using double-loop samplings.•The method developed is capable of solving large-scale engineering problems.
Details
- Title: Subtitle
- A surrogate method for density-based global sensitivity analysis
- Creators
- Sharif Rahman - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Reliability engineering & system safety, Vol.155, pp.224-235
- Publisher
- Elsevier Ltd
- DOI
- 10.1016/j.ress.2016.07.002
- ISSN
- 0951-8320
- eISSN
- 1879-0836
- Grant note
- 1462385 / U.S. National Science Foundation (http://dx.doi.org/10.13039/100000001)
- Language
- English
- Date published
- 11/2016
- Academic Unit
- Mechanical Engineering
- Record Identifier
- 9984196497502771
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