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SIAM/ASA Journal on Uncertainty Quantification is a peer-reviewed journal published by SIAM, covering Probabilistic and Robust Engineering Design, Advanced Multi-Objective Optimization Algorithms, Gaussian Processes and Bayesian Inference. Over its lifetime it has published 690 papers, which have received 9,940 citations.
| Publisher | SIAM |
|---|---|
| Reference style | Numbered (SIAM) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, and C. Lee, A representative article title, SIAM/ASA Journal on Uncertainty Quantification 12 (2023) 45–58.
Formats any DOI in the closest standard style — SIAM/ASA Journal on Uncertainty Quantification has no published style definition, so this is an approximation. No sign-up. |
| Publishes research in | Probabilistic and Robust Engineering Design Advanced Multi-Objective Optimization Algorithms Gaussian Processes and Bayesian Inference Model Reduction and Neural Networks Markov Chains and Monte Carlo Methods |
| ISSN | 2166-2525 |
| Citation impact (2-yr) | 1.96 |
| h-index | 49 |
| i10-index | 255 |
| Top institutions publishing here | Centre National de la Recherche Scientifique |
| Journal website | www.siam.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Global sensitivity analysis measures the importance of some input variables to a function $f$ by looking at the impact on $f$ of making large random perturbations to subsets of those variables. Using measures like those of Sobol' we can attribute importance to input variables based on the extent to which they help predict the target…
Sparse polynomial chaos expansions (PCE) are a popular surrogate modelling method that takes advantage of the properties of PCE, the sparsity-of-effects principle, and powerful sparse regression solvers to approximate computer models with many input parameters, relying on only few model evaluations. Within the last decade, a large number of algorithms for the computation of sparse…
Variance-based global sensitivity analysis decomposes the variance of the output of a computer model, resulting from uncertainty about the model's inputs, into variance components associated with each input's contribution. The two most common variance-based sensitivity measures, the first-order effects and the total effects, may fail to sum to the total variance. They are often used…
This paper makes the case for using Shapley value to quantify the importance of random input variables to a function. Alternatives based on the ANOVA decomposition can run into conceptual and computational problems when the input variables are dependent. Our main goal here is to show that Shapley value removes the conceptual problems. We do…
In this paper we address the problem of the prohibitively large computational cost of existing Markov chain Monte Carlo methods for large-scale applications with high-dimensional parameter spaces, e.g., in uncertainty quantification in porous media flow. We propose a new multilevel Metropolis--Hastings algorithm and give an abstract, problem-dependent theorem on the cost of the new multilevel…
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