Topic
Uncertainty quantification
About: Uncertainty quantification is a research topic. Over the lifetime, 8599 publications have been published within this topic receiving 132551 citations. The topic is also known as: UQ.
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TL;DR: In this paper, a nonparametric model of the generalized mass, damping and stiffness matrices is proposed, which does not require identifying the uncertain local parameters and obviates construction of functions that map the domains of uncertain local parameter vectors into the generalized matrix.
499 citations
TL;DR: A sensitivity analysis toolbox consisting of a set of Matlab functions that offer utilities for quantifying the influence of uncertain input parameters on uncertain model outputs is provided.
490 citations
TL;DR: In this article, the authors present a brief survey on some of the most relevant developments in the field of optimization under uncertainty, including reliability-based optimization, robust design optimization and model updating.
487 citations
07 Jul 2014
TL;DR: The modular platform comprises a highly optimized core probabilistic modelling engine and a simple programming interface that provides unified access to heterogeneous high performance computing resources and provides a content-management system that allows users to easily develop additional custom modules within the framework.
Abstract: Uncertainty quantification is a rapidly growing field in computer simulation-based scientific applications. The UQLAB project aims at the development of a MATLABbased software framework for uncertainty quantification. It is designed to encourage both academic researchers and field engineers to use and develop advanced and innovative algorithms for uncertainty quantification, possibly exploiting modern distributed computing facilities. Ease of use, extendibility and handling of non-intrusive stochastic methods are core elements of its development philosophy. The modular platform comprises a highly optimized core probabilistic modelling engine and a simple programming interface that provides unified access to heterogeneous high performance computing resources. Finally, it provides a content-management system that allows users to easily develop additional custom modules within the framework. In this contribution, we intend to demonstrate the features of the platform at its current development stage.
475 citations
TL;DR: In this article, an uncertainty quantification scheme based on generalized polynomial chaos (PC) representations is constructed, which is applied to a model problem involving a simplified dynamical system and to the classical problem of Rayleigh-Benard instability.
463 citations