International Journal for Uncertainty Quantification

Aims & Scope

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.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	1.8
2024	1.50

Journal Rank

Year	Value
2024	9191

Journal Citation Indicator

Year	Value
2024	124

SJR (SCImago Journal Rank)

Year	Value
2024	0.629

Quartile

Year	Value
2024	Q2

h-index

Year	Value
2024	29

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Mathematics, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• POLYNOMIAL-CHAOS-BASED KRIGING

  Citation: 285

  Authors: Roland, Bruno, Joe

• RECURSIVE CO-KRIGING MODEL FOR DESIGN OF COMPUTER EXPERIMENTS WITH MULTIPLE LEVELS OF FIDELITY

  Citation: 252

  Authors: Loic, Josselin

• DISTANCE MEASURES BETWEEN THE COMPLEX INTUITIONISTIC FUZZY SETS AND THEIR APPLICATIONS TO THE DECISION-MAKING PROCESS

  Citation: 141

  Authors: Dimple, Harish

• DIMENSIONALITY REDUCTION FOR COMPLEX MODELS VIA BAYESIAN COMPRESSIVE SENSING

  Citation: 133

  Authors: Khachik, Cosmin, Habib N., Bert J., Daniel, Peter

• A NEW IMPROVED SCORE FUNCTION OF AN INTERVAL-VALUED PYTHAGOREAN FUZZY SET BASED TOPSIS METHOD

  Citation: 113

  Authors: Harish

• STOCHASTIC COLLOCATION ALGORITHMS USING l1-MINIMIZATION

  Citation: 97

  Authors: Liang, Ling, Dongbin

• NOVEL SINGLE-VALUED NEUTROSOPHIC AGGREGATED OPERATORS UNDER FRANK NORM OPERATION AND ITS APPLICATION TO DECISION-MAKING PROCESS

  Citation: 93

  Authors: Harish

• CUBIC INTUITIONISTIC FUZZY AGGREGATION OPERATORS

  Citation: 90

  Authors: Gagandeep, Harish

• HESITANT PYTHAGOREAN FUZZY SETS AND THEIR AGGREGATION OPERATORS IN MULTIPLE ATTRIBUTE DECISION-MAKING

  Citation: 87

  Authors: Harish

• AN IMPROVED SCORE FUNCTION FOR RANKING NEUTROSOPHIC SETS AND ITS APPLICATION TO DECISION-MAKING PROCESS

  Citation: 83

  Authors: Harish

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