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International Journal for Uncertainty Quantification
Fator do impacto: 0.967 FI de cinco anos: 1.301 SJR: 0.531 SNIP: 0.8 CiteScore™: 1.52

ISSN Imprimir: 2152-5080
ISSN On-line: 2152-5099

International Journal for Uncertainty Quantification

Editor-in-Chief: Habib N. Najm

Associate Editors: Dongbin Xiu, Tao Zhou

Founding Editor: Nicholas Zabaras

Call for Papers: "Multilevel-Multifidelity Approaches for Uncertainty Quantification"

A special issue of the International Journal for Uncertainty Quantification. Submissions are open until summer 2019. Date of publication (estimated): late 2019/early 2020.

Computational simulation continues to advance in its predictive capability through the development of high-fidelity multi-scale/multi-physics simulation models executing on the latest high-performance computers. UQ methodologies are challenged in this environment, both by the prohibitive cost of computing high-fidelity ensembles and by the increasing random dimensionality induced by this model complexity. To address these challenges, researchers are effectively harnessing the utility that exists within hierarchies of model forms (multifidelity) and discretization levels (multilevel and multi-index) in order to balance multiple sources of error while intelligently allocating simulation resources. By relaxing the need for exclusive reliance on the most expensive models, high-fidelity UQ studies become tractable. This special issue will focus on the latest developments in multilevel, multifidelity, and multi-index algorithms, targeting both forward and inverse UQ analyses.

Editors of the Special Issue

Michael S. Eldred, Sandia National Laboratories
Gianluca Geraci, Sandia National Laboratories
Gianluca Iaccarino, Stanford University

To submit to this special issue, please register an author account through the Begell House Submission System and select "Submit to special issue" when submitting your article.

Finalidades e escopo

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.

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