Global sensitivity analysis of ordinary differential equations

TL;DRAbstract

Ordinary differential equations play an important role in the modeling of many real-world processes. To guarantee reliable results, model design and analysis must account for uncertainty and/or variability in the model input. The propagation of uncertainty & variability through the model dynamics and their effect on the output is studied by sensitivity analysis. Global sensitivity analysis is concerned with variations in the model input that possibly span a large domain. Two major problems that complicate the analysis are high- dimensionality and quality control, i.e. controlling the approximation error of the estimated output uncertainty. Current numerical approaches to global sensitivity analysis mainly focus on scalability to high-dimensional models. However, to what extent the estimated output uncertainty approximates the true output uncertainty generally remains unclear. In this thesis we suggest an error-controlled approach to global sensitivity analysis of ordinary different

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