The Multi-Level Monte Carlo finite volumes (MLMC-FVM) algorithm was shown to be a robust and fast solver for uncertainty quantification in the solutions of multi-dimensional systems of stochastic conservation laws. A novel load balancing procedure is used to ensure scalability of the MLMC algorithm on massively parallel hardware. We describe this procedure together with other arising challenges...

A technique for coupling an intrusive and non-intrusive uncertainty quantification method is proposed. The intrusive approach uses a combination of polynomial chaos and stochastic Galerkin projection. The non-intrusive method uses numerical integration by combining quadrature rules and the probability density functions of the prescribed uncertainties. A strongly stable coupling procedure betwee...

We present Korali, an open-source framework for large-scale Bayesian uncertainty quantification and stochastic optimization. The relies on non-intrusive sampling of complex multiphysics models enables their exploitation optimization decision-making. In addition, its distributed engine makes efficient use massively-parallel architectures while introducing novel fault tolerance load balancing mec...

performance evaluation of universities is an important issue between researchers. classic data envelopment analysis (dea) models with deterministic data have been used by many authors to measure efficiency of universities in different countries. however, dea with stochastic data are, rarely used to measure efficiency of universities. in this paper, input oriented model in stochastic data envelo...

Abstract: This paper presents and compares the results obtained using several methods for Stochastic Computations, used in Uncertainty Quantification. We practically present the methods using a simple ODE model. The focus is on both intrusive and non-intrusive methods, namely the Monte Carlo method, along with methods based on generalized polynomial chaos(gPC) methodology. Moreover, we asses th...