Variance-based sensitivity analysis for time-dependent processes

TL;DR

This paper introduces a variance-based sensitivity analysis method tailored for time-dependent processes, utilizing correlation structures and surrogate models to improve computational efficiency and accuracy.

Contribution

It presents a novel approach that combines variance-based sensitivity analysis with surrogate modeling for dynamic, history-aware systems.

Findings

Effective in harmonic oscillator example

Applicable to nonlinear cholera model

Provides error estimates for fixing unimportant parameters

Abstract

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.