Sensitivity analysis based dimension reduction of multiscale models

In this paper, the sensitivity analysis of a single scale model is employed in order to reduce the input dimensionality of the related multiscale model, in this way, improving the efficiency of its uncertainty estimation. The approach is illustrated with two examples: a reaction model and the standard Ornstein–Uhlenbeck process. Additionally, a counterexample shows that an uncertain input should not be excluded from uncertainty quantification without estimating the response sensitivity to this parameter. In particular, an analysis of the function defining the relation between single scale components is required to understand whether single scale sensitivity analysis can be used to reduce the dimensionality of the overall multiscale model input space

Белое движение в Сибири (1918–1923 гг.): рэферат к дипломной работе / Иван Антонович Квятковский; БГУ, Исторический факультет, Кафедра истории России, науч. рук. Кохнович В. А.

High proportion of children with MDR-TB had favourable outcome (90%) with early diagnosis and treatment initiation http://ow.ly/2eq5302gWm

Fluoroquinolone preventive therapy for children exposed to MDR-TB.

No Abstrac

Uncertainty quantification patterns for multiscale models

Uncertainty quantification (UQ) is a key component when using computational models that involve uncertainties, e.g. in decision-making scenarios. In this work, we present uncertainty quantification patterns (UQPs) that are designed to support the analysis of uncertainty in coupled multi-scale and multi-domain applications. UQPs provide the basic building blocks to create tailored UQ for multiscale models. The UQPs are implemented as generic templates, which can then be customized and aggregated to create a dedicated UQ procedure for multiscale applications. We present the implementation of the UQPs with multiscale co