Published by: Society for Industrial and Applied Mathematics
https://www.siam.org/publications/journals/siam-asa-journal-on-uncertainty-quantification-juq
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Mathematics | 208 | 41 | 56 | 12 |
| Engineering and Technology | 720 | 19 | 30 | 12 |
The journal is organized to address concerns in the fields of Applied mathematics, Uncertainty quantification, Mathematical optimization, Algorithm and Inverse problem. Topics in Applied mathematics explored in it were investigated in conjunction with research in Stochastic partial differential equation, Partial differential equation, Mathematical analysis, Finite element method and Monte Carlo method. SIAM/ASA Journal on Uncertainty Quantification links adjacent topics like Mathematical analysis with Random field.
The work tackled in it goes beyond the discipline of Monte Carlo method as it also encompasses Statistical physics. Discussions in it are anchored in the subject of Uncertainty quantification and the similar topic of Polynomial chaos. The research on Mathematical optimization tackled can also make contributions to studies in the areas of Discretization and Estimator.
In addition to Algorithm research, the journal aims to explore topics under Markov chain Monte Carlo, Gaussian process, Bayesian inference, Sampling (statistics) and Posterior probability. While the journal focused on Gaussian process, it was also able to explore topics like Covariance and Computer experiment. It focuses on Inverse problem research which is adjacent to topics in Bayesian probability.
The most cited papers generally zeroe in on subjects such as Applied mathematics, Mathematical optimization, Uncertainty quantification, Mathematical analysis and Gaussian process. The published papers with studies in Applied mathematics featured incorporate elements of Parametric statistics, Inverse problem, Partial differential equation, Kalman filter and Data assimilation. The works on Gaussian process tackled in the most cited articles bring together disciplines like Calibration (statistics), Algorithm and Computer experiment.
The scientific interests tackled in the journal are Applied mathematics, Uncertainty quantification, Algorithm, Partial differential equation and Monte Carlo method. The concepts on Applied mathematics presented in it can also apply to other research fields, including Inverse problem, Sensitivity (control systems), Probabilistic logic, Estimator and Function (mathematics). Some problems in Inverse problem that were presented in the journal overlapped with concepts under Regularization (mathematics), Mathematical optimization and Bayesian probability.
Remote sensing (archaeology), Remote sensing and State (computer science) are some topics wherein Uncertainty quantification research discussed in SIAM/ASA Journal on Uncertainty Quantification have an impact. While work presented in it provided substantial information on Algorithm, it also covered topics in Importance sampling, Optimal sampling, Markov chain Monte Carlo, Bayesian inference and Particle filter. SIAM/ASA Journal on Uncertainty Quantification holds forums on Monte Carlo method that merges themes from other disciplines such as Conservation law, Statistical physics and Random variable.
A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing in SIAM/ASA Journal on Uncertainty Quantification (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in SIAM/ASA Journal on Uncertainty Quantification (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2021 edition, 83.33% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 12.50% were posted by at least one author from the top 10 institutions publishing in the journal. Another 12.50% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 50.00% of all publications and 25.00% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
Sebastian Reich;Simon Weissmann
(2021)Nora Lüthen;Stefano Marelli;Bruno Sudret
(2021)Felipe Uribe;Iason Papaioannou;Youssef M. Marzouk;Daniel Straub
(2021)Philipp A. Guth;Vesa Kaarnioja;Frances Y. Kuo;Claudia Schillings
(2021)Amirhossein Taghvaei;Prashant G. Mehta;Sean P. Meyn
(2020)Colin J. Cotter;Dan Crisan;Darryl D. Holm;Wei Pan
(2020)Richard Nickl;Sara A. van de Geer;Sven Wang
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