《國際不確定性量化雜志》在存在不確定性的情況下，傳播對復(fù)雜系統(tǒng)的分析、建模、設(shè)計和控制領(lǐng)域具有永久性興趣的信息。該雜志力求強調(diào)交叉隨機分析、統(tǒng)計建模和科學(xué)計算的方法。感興趣的系統(tǒng)由可能具有多尺度特征的微分方程控制。特別感興趣的主題包括不確定性的表示、不確定性跨尺度傳播、解決維數(shù)的詛咒、隨機偏微分方程的長時間積分、構(gòu)建隨機模型的數(shù)據(jù)驅(qū)動方法、預(yù)測計算科學(xué)的驗證、驗證和不確定性量化，以及高維空間中不確定性的可視化。貝葉斯計算和機器學(xué)習(xí)技術(shù)也很有趣，例如在隨機多尺度系統(tǒng)的背景下，用于模型選擇/分類和決策。特別鼓勵有關(guān)現(xiàn)代實驗和預(yù)測科學(xué)建模方法的動態(tài)耦合的報告。不確定性量化在物理和生物科學(xué)的所有領(lǐng)域的應(yīng)用都是適當(dāng)?shù)摹?/p>

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.