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Journal of Verification, Validation and Uncertainty Quantification - The American Society of Mechanical Engineers | 2024 Impact Factor:0.8 | Cite Score:1.7 | Q4

Journal of Verification, Validation and Uncertainty Quantification With Cite Score

Cite Score and Journal Rank of Journal of Verification, Validation and Uncertainty Quantification

  • About: Journal of Verification, Validation and Uncertainty Quantification (VVUQ) is a peer-reviewed journal dedicated to the advancement of methods and practices related to verification, validation, and uncertainty quantification in computational modeling and simulations. The journal covers a wide range of topics including numerical verification, model validation, uncertainty quantification techniques, and their applications across various scientific and engineering domains. VVUQ aims to provide a comprehensive platform for research that enhances the accuracy, reliability, and credibility of computational models and simulations.
  • Objective:
    The primary objective of VVUQ is to advance the understanding and practice of verification, validation, and uncertainty quantification in computational modeling. The journal seeks to disseminate high-quality research that addresses the challenges associated with ensuring the correctness, accuracy, and reliability of computational models. VVUQ aims to support the development and application of robust methodologies for assessing and improving the quality of simulations and predictions in various scientific and engineering contexts.
  • Interdisciplinary Approach:
    VVUQ embraces an interdisciplinary approach, encouraging contributions from diverse fields such as applied mathematics, computational science, engineering, and statistics. This approach ensures a comprehensive exploration of verification, validation, and uncertainty quantification issues, integrating various perspectives and methodologies. By promoting interdisciplinary research, the journal addresses complex challenges and fosters the development of integrated solutions that enhance the reliability and accuracy of computational models and simulations.
  • Impact:
    The journal has a significant impact on both academic research and practical applications in computational modeling and simulation. It is widely cited by researchers, practitioners, and industry professionals interested in the latest developments in verification, validation, and uncertainty quantification. The research published in VVUQ contributes to the advancement of new techniques and methodologies that improve the quality and reliability of computational predictions. The journal serves as a valuable resource for professionals involved in the development, implementation, and assessment of computational models.
  • Significance:
    VVUQ plays a crucial role in advancing the study and practice of verification, validation, and uncertainty quantification by providing a platform for high-quality research and practical insights. Its contributions support the development of innovative approaches and methodologies that address current and future challenges in computational modeling. The journals commitment to excellence and interdisciplinary focus make it an essential resource for anyone involved in research, development, and application in the field of computational modeling and simulations. Through its rigorous scholarship and broad coverage, VVUQ helps shape the future of computational science, driving progress in ensuring the accuracy and reliability of simulation-based predictions.

  • Editor-in-Chief:  Aaron Koskelo

  • Scope: The Journal of Verification, Validation and Uncertainty Quantification (VVUQ) focuses on research related to the verification, validation, and uncertainty quantification of computational models and simulations. Its scope includes, but is not limited to:
  • Verification: Research on techniques for verifying computational models and simulations, including code verification, numerical method verification, and verification against known solutions.
  • Validation: Studies on the validation of models and simulations, including comparison with experimental data, model calibration, and validation strategies.
  • Uncertainty Quantification (UQ): Exploration of methods for quantifying and managing uncertainty in computational models, including uncertainty propagation, sensitivity analysis, and stochastic modeling.
  • Modeling and Simulation: Research on the development, analysis, and improvement of models and simulations, including both physical and mathematical models.
  • Statistical Methods: Studies on statistical techniques for assessing model accuracy, including Bayesian methods, statistical inference, and error analysis.
  • Numerical Methods: Exploration of numerical techniques for solving and analyzing computational models, including finite element methods, finite difference methods, and meshless methods.
  • High-Performance Computing: Research on the use of high-performance computing (HPC) for verification, validation, and uncertainty quantification, including parallel computing and scalability.
  • Application Areas: Case studies and applications of VVUQ in various fields, including aerospace, automotive, civil engineering, climate modeling, and biomedical engineering.
  • Software and Tools: Development and evaluation of software tools and frameworks for verification, validation, and uncertainty quantification, including open-source tools and commercial software.
  • Methodology and Frameworks: Research on methodologies and frameworks for systematic VVUQ, including best practices, standards, and guidelines.
  • Uncertainty Analysis: Techniques for analyzing and managing uncertainty in model inputs, parameters, and outputs, including propagation methods and sensitivity analysis.
  • Data Assimilation: Research on techniques for integrating observational data with computational models, including data assimilation methods and algorithm development.
  • Latest Research Topics for PhD in Computer Science
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  • Print ISSN:  2377-2158

    Electronic ISSN:  2377-2166

  • Abstracting and Indexing:  Scopus

  • Imapct Factor 2024:  0.8

  • Subject Area and Category:  Computer Science, Computational Theory and Mathematics, Computer Science Applications, Mathematics, Modeling and Simulation, Statistics and Probability

  • Publication Frequency:  

  • H Index:  15

  • Best Quartile:

    Q1:  

    Q2:  

    Q3:  

    Q4:  Computational Theory and Mathematics

  • Cite Score:  1.7

  • SNIP:  0.622

  • Journal Rank(SJR):  0.180