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Variance-based sensitivity analysis for time-dependent processes
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DOI：10.1016/j.ress.2019.106722.png)
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Abstract
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The global sensitivity analysis of time-dependent processes requires history-aware approaches. We develop for that purpose a variance-based method that leverages the correlation structure of the problems under study and employs surrogate models to accelerate the computations. The errors resulting from fixing unimportant uncertain parameters to their nominal values are analyzed through a priori estimates. We illustrate our approach on a harmonic oscillator example and on a nonlinear dynamic cholera model.
Keywords:
Global sensitivity analysis
Sobol' Indices
Karhunen-Loeve expansion
Time-dependent processes
Surrogate models
Polynomial chaos
Uncertainty quantification
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