Home Books eBooks Journals References & Proceedings Authors, Editors, Reviewers A-Z Product Index Awards
International Journal for Uncertainty Quantification
Impact Factor: 1.000

ISSN Print: 2152-5080
ISSN Online: 2152-5099

Volume 8, 2018

Volume 7, 2017

Volume 6, 2016

Volume 5, 2015

Volume 4, 2014

Volume 3, 2013

Volume 2, 2012

Volume 1, 2011

Issue 1

Issue 2

Issue 3

Issue 4

International Journal for Uncertainty Quantification

Editor-in-Chief: Nicholas Zabaras

Associate Editor: Dongbin Xiu

Aims and Scope

The International Journal for Uncertainty Quantification disseminates information of permanent interest in the areas of analysis, modeling, design and control of complex systems in the presence of uncertainty. The journal seeks to emphasize methods that cross stochastic analysis, statistical modeling and scientific computing. Systems of interest are governed by differential equations possibly with multiscale features. Topics of particular interest include representation of uncertainty, propagation of uncertainty across scales, resolving the curse of dimensionality, long-time integration for stochastic PDEs, data-driven approaches for constructing stochastic models, validation, verification and uncertainty quantification for predictive computational science, and visualization of uncertainty in high-dimensional spaces. Bayesian computation and machine learning techniques are also of interest for example in the context of stochastic multiscale systems, for model selection/classification, and decision making. Reports addressing the dynamic coupling of modern experiments and modeling approaches towards predictive science are particularly encouraged. Applications of uncertainty quantification in all areas of physical and biological sciences are appropriate.

Most Downloaded Articles

VISUALIZING UNCERTAINTY IN PREDICTED HURRICANE TRACKS
Jonathan Cox, Donald House, Michael Lindell

IMPROVEMENTS TO GRADIENT-ENHANCED KRIGING USING A BAYESIAN INTERPRETATION
Jouke H.S. de Baar, Richard P. Dwight, Hester Bijl

RECURSIVE CO-KRIGING MODEL FOR DESIGN OF COMPUTER EXPERIMENTS WITH MULTIPLE LEVELS OF FIDELITY
Loic Le Gratiet, Josselin Garnier

PINK NOISE, 1/fα NOISE, AND THEIR EFFECT ON SOLUTIONS OF DIFFERENTIAL EQUATIONS
Miroslav Stoyanov, Max Gunzburger, John Burkardt

POLYNOMIAL-CHAOS-BASED KRIGING
Roland Schobi, Bruno Sudret, Joe Wiart

GRADIENT-BASED STOCHASTIC OPTIMIZATION METHODS IN BAYESIAN EXPERIMENTAL DESIGN
Xun Huan, Youssef Marzouk