2152-5080
Published by: Begell House
https://www.begellhouse.com/journals/uncertainty-quantification.html
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Engineering and Technology | 756 | 30 | 43 | 11 |
Uncertainty quantification, Mathematical optimization, Applied mathematics, Algorithm and Artificial intelligence are the subjects of interest in the journal. Some problems in Uncertainty quantification that were presented in it overlapped with concepts under Kriging, Monte Carlo method, Polynomial chaos and Computational model. The studies tackled, which mainly focus on Monte Carlo method, apply to Finite element method as well.
The journal holds forums on Mathematical optimization that merges themes from other disciplines such as Probability distribution, Markov chain Monte Carlo, Estimation theory and Nonlinear system. Collocation method and Collocation are some topics wherein Applied mathematics research discussed in it have an impact. Algorithm research featured in it incorporates concerns from various other topics such as Sensitivity (control systems), Inference, Surrogate model and Bayesian inference.
The works on Sensitivity (control systems) deal in particular with Sobol sequence. The Bayesian inference research presented falls under the domain of Bayesian probability. Machine learning and Pattern recognition are some topics wherein Artificial intelligence research discussed in the journal have an impact.
The published articles cover a variety of subjects, including Artificial intelligence, Mathematical optimization, Machine learning, Algorithm and Uncertainty quantification. While the primary focus in the journal papers is Artificial intelligence, they also dissect topics surrounding Pattern recognition and Multiple attribute as a whole. While Uncertainty quantification is the focus of the journal papers, it also provides insights into the studies of Computational model, Surrogate model and Polynomial chaos.
The journal primarily focuses on research topics in Uncertainty quantification, Algorithm, Applied mathematics, Monte Carlo method and Gaussian process. While it primarily focused on Uncertainty quantification, it also opened dialogues on the discipline of Resonance (particle physics). It features studies on Algorithm, including topics such as Computational model.
The research on Applied mathematics tackled can also make contributions to studies in the areas of Numerical analysis, Random variable, Partial differential equation, Polynomial chaos and Chaotic systems. The research on Monte Carlo method featured in the journal combines topics in other fields like Conservation law, Galerkin method, Sampling (statistics), Bending and Neumann series. It addresses concerns in Calibration (statistics) which are intertwined with other disciplines, such as Mathematical optimization and Artificial intelligence.
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 International Journal for 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 International Journal for 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, 28.57% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 36.00% were posted by at least one author from the top 10 institutions publishing in the journal. Another 8.00% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 24.00% of all publications and 32.00% 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.
Christos Lataniotis;Stefano Marelli;Bruno Sudret
(2020)Tong Qin;Zhen Chen;John D. Jakeman;Dongbin Xiu
(2021)Arnald Puy;William Becker;Samuele Lo Piano;Andrea Saltelli
(2021)Xujia Zhu;Bruno Sudret
(2020)Stefano Marelli;Paul-Remo Wagner;Christos Lataniotis;Bruno Sudret
(2021)Nora Lüthen;Stefano Marelli;Bruno Sudret
(2021)Santiago Badia;Jerrad Hampton;Javier Principe
(2021)Jongmin Seo;Casey Fleeter;Andrew M. Kahn;Alison L. Marsden
(2020)Maarten Arnst;Christian Soize;Kevin Bulthuis
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