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International Journal for Uncertainty Quantification

Publication de 6  numéros par an

ISSN Imprimer: 2152-5080

ISSN En ligne: 2152-5099

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.9 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.5 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.0007 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.5 SJR: 0.584 SNIP: 0.676 CiteScore™:: 3 H-Index: 25

Indexed in

SECOND-ORDER SENSITIVITY ANALYSIS OF PARAMETER ESTIMATION PROBLEMS

Volume 5, Numéro 3, 2015, pp. 209-231
DOI: 10.1615/Int.J.UncertaintyQuantification.2015010312
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RÉSUMÉ

The use of model-based simulation to gain knowledge of unknown phenomena and processes behavior is a challenging task in many natural sciences. In order to get a full description of an underlying process, an important issue is to estimate unknown parameters from real but erroneous observations. Thus the whole system is affected by uncertainties and a sensitivity analysis is necessary. Usually one applies first-order sensitivity analysis and resulting linearized confidence regions to determine the statistical accuracy of the solution to parameter estimation problems. But especially in significantly nonlinear cases linearized regions may not be an adequate representation. In this paper, we suggest quadratic regions based on the second-order sensitivity analysis. The new region definition is based on a map that transforms the input uncertainties onto the parameter space. Furthermore, the approximation accuracy of the quadratic confidence regions is exemplary illustrated at two examples.

CITÉ PAR
  1. Carraro Thomas, Olkhovskiy Vladislav, Successive Approximation of Nonlinear Confidence Regions (SANCR), in System Modeling and Optimization, 494, 2016. Crossref

  2. Coulibaly Anna, Bettendorf Anja, Kostina Ekaterina, Figueiredo Ana Sofia, Velásquez Sonia Y., Bock Hans-Georg, Thiel Manfred, Lindner Holger A., Barbarossa Maria Vittoria, Interleukin-15 Signaling in HIF-1α Regulation in Natural Killer Cells, Insights Through Mathematical Models, Frontiers in Immunology, 10, 2019. Crossref

  3. Krausch Niels, Barz Tilman, Sawatzki Annina, Gruber Mathis, Kamel Sarah, Neubauer Peter, Cruz Bournazou Mariano Nicolas, Monte Carlo Simulations for the Analysis of Non-linear Parameter Confidence Intervals in Optimal Experimental Design, Frontiers in Bioengineering and Biotechnology, 7, 2019. Crossref

  4. Bock Hans Georg, Kostina Ekaterina, Sauter Marta, Schlöder Johannes P., Schlöder Matthias, Numerical Methods for Diagnosis and Therapy Design of Cerebral Palsy by Bilevel Optimal Control of Constrained Biomechanical Multi-Body Systems, in Non-Smooth and Complementarity-Based Distributed Parameter Systems, 172, 2022. Crossref

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