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.

《International Journal For Uncertainty Quantification》是一本由BEGELL HOUSE INC出版商出版的专业工程技术期刊，该刊创刊于2011年，刊期6 issues/year，该刊已被国际权威数据库SCIE收录。在中科院最新升级版分区表中，该刊分区信息为大类学科：工程技术 4区，小类学科：工程：综合 4区；数学跨学科应用 4区；在JCR（Journal Citation Reports）分区等级为Q3。该刊发文范围涵盖工程：综合等领域，旨在及时、准确、全面地报道国内外工程：综合工作者在该领域取得的最新研究成果、工作进展及学术动态、技术革新等，促进学术交流，鼓励学术创新。2021年影响因子为1.544，