インパクトファクター: 3.259 5年インパクトファクター: 2.547 SJR: 0.531 SNIP: 0.8 CiteScore™: 1.52
ISSN 印刷: 2152-5080
巻 9, 2019
巻 8, 2018
巻 7, 2017
巻 6, 2016
巻 5, 2015
巻 4, 2014
巻 3, 2013
巻 2, 2012
巻 1, 2011
International Journal for Uncertainty Quantification
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
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.
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.
DIMENSIONALITY REDUCTION FOR COMPLEX MODELS VIA BAYESIAN COMPRESSIVE SENSING
RECURSIVE CO-KRIGING MODEL FOR DESIGN OF COMPUTER EXPERIMENTS WITH MULTIPLE LEVELS OF FIDELITY
GRADIENT-BASED STOCHASTIC OPTIMIZATION METHODS IN BAYESIAN EXPERIMENTAL DESIGN
VISUALIZING UNCERTAINTY IN PREDICTED HURRICANE TRACKS
ERROR AND UNCERTAINTY QUANTIFICATION AND SENSITIVITY ANALYSIS IN MECHANICS COMPUTATIONAL MODELS