Self-validated variance-based methods for sensitivity analysis of model outputs

Abstract

Global sensitivity analysis (GSA) has the advantage over local sensitivity analysis in that GSA does not require strong model assumptions such as linearity or monotonicity. As a result, GSA methods such as those based on variance decomposition are well-suited to multi-physics models, which are often plagued by large nonlinearities. However, as with many other sampling-based methods, inadequate sample size can badly pollute the result accuracies. A natural remedy is to adaptively increase the sample size until sufficient accuracy is obtained. This paper proposes an iterative methodology comprising mechanisms for guiding sample size selection and self-assessing result accuracy. The elegant features in the the proposed methodology are the adaptive refinement strategies for stratified designs. We first apply this iterative methodology to the design of a self-validated first-order sensitivity analysis algorithm. We also extend this methodology to design a self-validated second-order sensitivity analysis algorithm based on refining replicated orthogonal array designs. Several numerical experiments are given to demonstrate the effectiveness of these methods.

Uncertainty assessment approach for composite structures based on global sensitivity indices

“…Among GSA techniques the variance-based methods are the most appropriate [12,13,28]. GSA studies the effects of input variations on model outputs in the entire allowable ranges of the input space.…”

“…The local measures of sensitivity are not enough for a full evaluation of the influence of input parameters on structural response uncertainty [12][13][14]. The uncertainty analysis on response in the neighborhood of mean values of input parameters is of limited value.…”

“…cal sensitivity analysis in that GSA does not require strong model assumptions such as linearity or monotonicity [13,28]. However its application for composite structures is complex and expensive from the computational point of view.…”

“…The uncertainty propagation of composite structures is investigated in this work considering descriptive statistical measures of the response variability and sensitivity analysis of system responses inside GSA framework [11][12][13]. A study based on sensitivity to uncertainty that allows selecting the important parameters using global sensitivity indices is presented.…”

Global Sensitivity Analysis to Assess Salt Precipitation for CO2 Geological Storage in Deep Saline Aquifers

“…The conventional approach to performing global sensitivity analysis is the Morris sensitivity test method, the Fourier amplitude sensitivity test (FAST), and the Sobol sensitivity test method [13][14][15]. Global sensitivity analysis has been widely used in hydrological models and design models [16,17]. Global sensitivity analysis requires a large number of model calculations, and the salt precipitation model for geological storage is very complex; thus, it is very difficult to calculate the global sensitivity of salt precipitation.…”