comprehensive quantification of uncertainties and errors from all phases of the modeling and simulation process. Uncertainty and error quantification is a two-step process, the first step being the identification of all uncertainty and error sources in each phase of modeling and simulation. The second step

expansion method is explained and applied to estimate the scattered electric field from a building facade having 8 random parameters, in specular and non-specular scenarios. Uncertainty analysis and total output distribution are discussed in different diffraction zones of the building. Index Terms

Random parameters Probability density function Hyperbolic conservation law a b s t r a c t We develop a probabilistic approach to quantify parametric uncertainty in first-order hyperbolic conservation laws (kinematic wave equations). The approach relies on the der-ivation of a deterministic

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Non-intrusive Uncertainty Quantification (UQ) has the advantage that it can readily be applied to many differ-ent problems. Here the focus is mainly on the results of successful applications of uncertainty quantification in energy. Renewable electrical energy sources are necessary to reduce our

Abstract Optimal uncertainty quantification (OUQ) is a framework for numerical extreme-case analysis of stochastic systems with imperfect knowledge of the underlying probability distribution and functions/events. This paper presents sufficient conditions (when underlying functions are known) under

This session offers an open forum to discuss issues and directions of research in the areas of model updating, predictive quality of computer simulations, model validation and uncertainty quantification. Technical presentations review the state-of-the-art in nonlinear dynamics and model validation

This paper presents a basic tutorial on epistemic uncertainty quantification methods. Epistemic uncertainty, characterizing lack-of-knowledge, is often prevalent in engineering applications. However, the methods we have for analyzing and propagating epistemic uncertainty are not as nearly widely

The authors ’ equation- and Galerkin-free computational approach to uncertainty quantification for dynamical systems conducts UQ computations using short bursts of appropriately initialized ensembles of simulations. Their basic procedure estimates the quantities arising in stochastic Galerkin

the thickness of a panel. However the parameters for joint models (for example) must be estimated from measurements using the techniques of finite element model updating. In these cases the techniques of model updating must be extended to allow for uncertainty quantification from a series of measurements