scholarly journals Derivative-Based Global Sensitivity Analysis for Models with High-Dimensional Inputs and Functional Outputs

2019 ◽  
Vol 41 (6) ◽  
pp. A3524-A3551 ◽  
Author(s):  
Helen L. Cleaves ◽  
Alen Alexanderian ◽  
Hayley Guy ◽  
Ralph C. Smith ◽  
Meilin Yu
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
High Dimensional ◽  
Global Sensitivity

Effective Global Sensitivity Analysis for High-Dimensional Hydrologic and Water Quality Models

2019 ◽  
Vol 24 (1) ◽  
pp. 04018057 ◽  
Author(s):  
Yogesh Khare ◽  
Christopher J. Martinez ◽  
Rafael Muñoz-Carpena ◽  
Adelbert “Del” Bottcher ◽  
Andrew James
Keyword(s):  
Water Quality ◽  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
High Dimensional ◽  
Quality Models ◽  
Global Sensitivity ◽  
Water Quality Models

Analytical global sensitivity analysis with Gaussian processes

2017 ◽  
Vol 31 (3) ◽  
pp. 235-250 ◽  
Author(s):  
Ankur Srivastava ◽  
Arun K. Subramaniyan ◽  
Liping Wang
Keyword(s):  
Sensitivity Analysis ◽  
Gaussian Processes ◽  
Global Sensitivity Analysis ◽  
Decomposition Methods ◽  
Variance Decomposition ◽  
Model Representation ◽  
High Dimensional ◽  
High Dimensional Model Representation ◽  
Sobol Indices ◽  
Global Sensitivity

AbstractMethods for efficient variance-based global sensitivity analysis of complex high-dimensional problems are presented and compared. Variance decomposition methods rank inputs according to Sobol indices that can be computationally expensive to evaluate. Main and interaction effect Sobol indices can be computed analytically in the Kennedy and O'Hagan framework with Gaussian processes. These methods use the high-dimensional model representation concept for variance decomposition that presents a unique model representation when inputs are uncorrelated. However, when the inputs are correlated, multiple model representations may be possible leading to ambiguous sensitivity ranking with Sobol indices. In this work, we present the effect of input correlation on sensitivity analysis and discuss the methods presented by Li and Rabitz in the context of Kennedy and O'Hagan's framework with Gaussian processes. Results are demonstrated on simulated and real problems for correlated and uncorrelated inputs and demonstrate the utility of variance decomposition methods for sensitivity analysis.

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