Chen, Xiaoxiao Ph.D., Purdue University, December 2014. Epistemic Uncertainty Quantification in Scientific Models. Major Professor: Dongbin Xiu. In the field of uncertainty quantification (UQ), epistemic uncertainty often refers to the kind of uncertainty whose complete probabilistic description is not available, largely due to our lack of knowledge about the uncertainty. Quantification of the ...

The proposed approach aims to develop a new method of forming and processing of multiple hypotheses for initial orbit determination using optical observations. This method allows us to generalize the existing 2-dimensional flat constrained admissible region (CAR) to a unique 3-dimensional (3D) manifold of points corresponding to the pairs of observed right ascension and declination. Another adv...

The UQ RoboRoos have been developed to participate in the RoboCup robot soccer small size league. This paper describes the current level of implementation of the robots, including aspects of hardware design, as well as the software running on the robots and the controlling computer. Key features of the RoboRoos design include the agile and powerful mechanical frame, the robots’ navigational tec...

Effective uncertainty quantification (UQ) begins at the earliest phase in the design phase for which there are adequate models and continues tightly integrated to the analysis and design cycles as the refinement of the models and the fidelity of the tools increase. It is essential that uncertainty quantification strategies provide objective information to support the processes of identifying, a...

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...

An engineering analysis requires a realistic quantification of all input information. The amount and quality of the available information dictates the uncertainty model and its associated quantification concept. For inconsistent information, a distinction between probabilistic and non-probabilistic characteristics is beneficial. In this distinction, uncertainty refers to probabilistic character...

A variety of methods is available to quantify uncertainties arising within the modeling of flow and transport in carbon dioxide storage, but there is a lack of thorough comparisons. Usually, raw data from such storage sites can hardly be described by theoretical statistical distributions since only very limited data is available. Hence, exact information on distribution shapes for all uncertain...

Prevailing Modeling and Simulation (M&S) techniques have struggled to provide meaningful quantitative results in M&S of complex System of Systems (SoSs). This paper reports on systems thinking applied to “how” M&S techniques should shift to allow a next generation of quantitative tools and techniques. The imperative is to provide quantitative performance results across the constituent interface...

This paper outlines a methodology for Bayesian multimodel uncertainty quantification (UQ) and propagation and presents an investigation into the effect of prior probabilities on the resulting uncertainties. The UQ methodology is adapted from the information-theoretic method previously presented by the authors (Zhang and Shields, 2018) to a fully Bayesian construction that enables greater flexib...