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International Journal for Uncertainty Quantification
基本信息
期刊名称 | International Journal for Uncertainty Quantification INT J UNCERTAIN QUAN |
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期刊ISSN | 2152-5080 |
期刊官方网站 | http://uncertainty-quantification.com/ |
是否OA | No |
出版商 | Begell House Inc. |
出版周期 | |
文章处理费 | 登录后查看 |
始发年份 | |
年文章数 | 22 |
最新影响因子 | 1.5(2023) scijournal影响因子 greensci影响因子 |
中科院SCI期刊分区
大类学科 | 小类学科 | Top | 综述 |
---|---|---|---|
工程技术4区 | ENGINEERING, MULTIDISCIPLINARY 工程:综合3区 | 否 | 否 |
MATHEMATICS, INTERDISCIPLINARY APPLICATIONS 数学跨学科应用3区 |
CiteScore
CiteScore排名 | CiteScore | SJR | SNIP | ||
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学科 | 排名 | 百分位 | 3.6 | 0.715 | 0.759 |
Mathematics Discrete Mathematics and Combinatorics |
5/92 | 95% |
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Mathematics Statistics and Probability |
59/278 | 78% |
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Mathematics Control and Optimization |
40/130 | 69% |
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Mathematics Modeling and Simulation |
132/324 | 59% |
补充信息
自引率 | 6.7% |
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H-index | 14 |
SCI收录状况 |
Science Citation Index Expanded |
官方审稿时间 | 登录后查看 |
网友分享审稿时间 | 数据统计中,敬请期待。 |
接受率 | 登录后查看 |
PubMed Central (PMC) | http://www.ncbi.nlm.nih.gov/nlmcatalog?term=2152-5080%5BISSN%5D |
投稿指南
期刊投稿网址 | http://www.submission.begellhouse.com/usr/login.html?prod_code=ijuq |
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收稿范围 | 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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