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SIAM-ASA Journal on Uncertainty Quantification : Impact Factor & More

eISSN: 2166-2525pISSN: 2166-2525

Aims and Scope of SIAM-ASA Journal on Uncertainty Quantification

"SIAM/ASA Journal on Uncertainty Quantification (JUQ) publishes research articles presenting significant mathematical, statistical, algorithmic, and application advances in uncertainty quantification, defined as the interface of complex modeling of processes and data, especially characterizations of the uncertainties inherent in the use of such models. The journal also focuses on related fields such as sensitivity analysis, model validation, model calibration, data assimilation, and code verification. The journal also solicits papers describing new ideas that could lead to significant progress in methodology for uncertainty quantification as well as review articles on particular aspects. The journal is dedicated to nurturing synergistic interactions between the mathematical, statistical, computational, and applications communities involved in uncertainty quantification and related areas. JUQ is jointly offered by SIAM and the American Statistical Association." Less

Key Metrics

CiteScore
3.9
Impact Factor
< 5
SJR
Q2Applied Mathematics
SNIP
1.47
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Topics Covered on SIAM-ASA Journal on Uncertainty Quantification

SIAM-ASA Journal on Uncertainty Quantification Journal Specifications

Indexed in the following public directories

  • Web of Science Web of Science
  • Scopus Scopus
  • Inspec Inspec
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Overview
Publisher SIAM PUBLICATIONS
Language English
Frequency Quarterly
General Details
LanguageEnglish
FrequencyQuarterly
Publication Start Year2013
Publisher URLVisit website
Website URLVisit website
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Recently Published Papers in SIAM-ASA Journal on Uncertainty Quantification

Tensor-Variate Gaussian Process Regression for Efficient Emulation of Complex Systems: Comparing Regressor and Covariance Structures in Outer Product and Parallel Partial Emulators
  • 20 Apr 2026
  • SIAM/ASA Journal on Uncertainty Quantification
<tt>PCE-Net</tt> : High-Dimensional Surrogate Modeling for Learning Uncertainty
  • 26 Feb 2026
  • SIAM/ASA Journal on Uncertainty Quantification
Iterative Methods for Full-Scale Gaussian Process Approximations for Large Spatial Data
  • 23 Feb 2026
  • SIAM/ASA Journal on Uncertainty Quantification
Subspace Splitting Fast Sampling from Gaussian Posterior Distributions of Linear Inverse Problems
  • 3 Feb 2026
  • SIAM/ASA Journal on Uncertainty Quantification
Random Fourier Features Based Gaussian Process Models for Stochastic Simulations
  • 6 Jan 2026
  • SIAM/ASA Journal on Uncertainty Quantification
Goal-Oriented Bayesian Optimal Experimental Design for Nonlinear Models Using Markov Chain Monte Carlo
  • 2 Jan 2026
  • SIAM/ASA Journal on Uncertainty Quantification
Tensor-Variate Gaussian Process Regression for Efficient Emulation of Complex Systems: Comparing Regressor and Covariance Structures in Outer Product and Parallel Partial Emulators
  • 20 Apr 2026
  • SIAM/ASA Journal on Uncertainty Quantification
<tt>PCE-Net</tt> : High-Dimensional Surrogate Modeling for Learning Uncertainty
  • 26 Feb 2026
  • SIAM/ASA Journal on Uncertainty Quantification
Iterative Methods for Full-Scale Gaussian Process Approximations for Large Spatial Data
  • 23 Feb 2026
  • SIAM/ASA Journal on Uncertainty Quantification
Subspace Splitting Fast Sampling from Gaussian Posterior Distributions of Linear Inverse Problems
  • 3 Feb 2026
  • SIAM/ASA Journal on Uncertainty Quantification
Random Fourier Features Based Gaussian Process Models for Stochastic Simulations
  • 6 Jan 2026
  • SIAM/ASA Journal on Uncertainty Quantification
Goal-Oriented Bayesian Optimal Experimental Design for Nonlinear Models Using Markov Chain Monte Carlo
  • 2 Jan 2026
  • SIAM/ASA Journal on Uncertainty Quantification

FAQs on SIAM-ASA Journal on Uncertainty Quantification