We present an information-based uncertainty quantification method for general Markov random fields (MRFs). MRFs are structured, probabilistic graphical models over undirected graphs and provide a f...

This report investigates a technique to calculate the distributions of discretization errors for a model of advection-diffusion-reaction with stochastic noise in problem data. The focus is on operator-split discretization methods. The error is decomposed into components due to the splitting and due to the discretization within each component. We present a method to estimate the distributions of...

In Operational Modal Analysis, the modal parameters (natural frequencies, damping ratios and mode shapes), obtained from Stochastic Subspace Identification of structures, are subject to statistical uncertainty from ambient vibration measurements. It is hence neccessary to evaluate the confidence intervals of these obtained results. This paper will propose an algorithm that can efficiently estim...

This paper describes the use of a set inversion algorithm to solve the problem of mobile robot localization. The method is based on the formalism of interval analysis. In this formalism, an imprecise number is represented by an interval which contains it in a guaranteed way. This enables to naturally manage the imprecision linked to the mobile robot configuration. Indeed, we show that imprecisi...

Abstract: Multivariate polynomial metamodels are widely used for uncertainty quantification due to the development of polynomial chaos methods and stochastic collocation. However, these metamodels only provide point predictions. There is no known method that can quantify interpolation error probabilistically and design interpolation points using available data to reduce the error. We shall intr...

Monte Carlo and Active Subspace Identification methods are combined with firstand second-order adjoint sensitivities to perform (forward) uncertainty quantification analysis of the thermo-acoustic stability of two annular combustor configurations. This method is applied to evaluate the risk factor, i.e., the probability for the system to be unstable. It is shown that the adjoint approach reduce...