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.

《国际不确定性量化杂志》传播了在存在不确定性的情况下对复杂系统进行分析、建模、设计和控制等领域的长期关注信息。该杂志力求强调跨随机分析、统计建模和科学计算的方法。感兴趣的系统由可能具有多尺度特征的微分方程控制。特别感兴趣的主题包括不确定性的表示、不确定性在尺度上的传播、解决维数灾难、随机偏微分方程的长时间积分、构建随机模型的数据驱动方法、预测计算科学的验证、确认和不确定性量化以及高维空间中不确定性的可视化。贝叶斯计算和机器学习技术也很有趣，例如在随机多尺度系统的背景下，用于模型选择/分类和决策。特别鼓励提交关于现代实验和建模方法与预测科学的动态耦合的报告。不确定性量化在物理和生物科学的所有领域中的应用都是合适的。

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