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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

DATA-FREE INFERENCE OF UNCERTAIN PARAMETERS IN CHEMICAL MODELS

Volume 4, Numéro 2, 2014, pp. 111-132
DOI: 10.1615/Int.J.UncertaintyQuantification.2013005679
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RÉSUMÉ

We outline the use of a data-free inference procedure for estimation of uncertain model parameters for a chemical model of methane-air ignition. The method involves a nested pair of Markov chains, exploring both the data and parametric spaces, to discover a pooled joint posterior consistent with available information. We describe the highlights of the method, and detail its particular implementation in the system at hand. We examine the performance of the procedure, focusing on the robustness and convergence of the estimated joint parameter posterior with increasing number of data chain samples. We also comment on comparisons of this posterior with the missing reference posterior density.

CITÉ PAR
  1. Sargsyan K., Najm H. N., Ghanem R., On the Statistical Calibration of Physical Models, International Journal of Chemical Kinetics, 47, 4, 2015. Crossref

  2. Khalil M., Chowdhary K., Safta C., Sargsyan K., Najm H.N., Inference of reaction rate parameters based on summary statistics from experiments, Proceedings of the Combustion Institute, 36, 1, 2017. Crossref

  3. Najm Habib, Chowdhary Kenny, Inference Given Summary Statistics, in Handbook of Uncertainty Quantification, 2015. Crossref

  4. Kim Daesang, El Gharamti Iman, Hantouche Mireille, Elwardany Ahmed E., Farooq Aamir, Bisetti Fabrizio, Knio Omar, A hierarchical method for Bayesian inference of rate parameters from shock tube data: Application to the study of the reaction of hydroxyl with 2-methylfuran, Combustion and Flame, 184, 2017. Crossref

  5. Najm Habib N., Chowdhary Kenny, Inference Given Summary Statistics, in Handbook of Uncertainty Quantification, 2017. Crossref

  6. Khalil Mohammad, Najm Habib N., Probabilistic inference of reaction rate parameters from summary statistics, Combustion Theory and Modelling, 22, 4, 2018. Crossref

  7. Casey Tiernan A., Najm Habib N., Estimating the joint distribution of rate parameters across multiple reactions in the absence of experimental data, Proceedings of the Combustion Institute, 37, 1, 2019. Crossref

  8. Soize Christian, Ghanem Roger, Physics‐constrained non‐Gaussian probabilistic learning on manifolds, International Journal for Numerical Methods in Engineering, 121, 1, 2020. Crossref

  9. Torres-Herrador Francisco, Coheur Joffrey, Panerai Francesco, Magin Thierry E., Arnst Maarten, Mansour Nagi N., Blondeau Julien, Competitive kinetic model for the pyrolysis of the Phenolic Impregnated Carbon Ablator, Aerospace Science and Technology, 100, 2020. Crossref

  10. Hantouche Mireille, Sarathy S. Mani, Knio Omar M., Global sensitivity analysis of n-butanol ignition delay times to thermodynamics class and rate rule parameters, Combustion and Flame, 222, 2020. Crossref

  11. Walker Eric A., Ravisankar Kishore, Savara Aditya, CheKiPEUQ Intro 2: Harnessing Uncertainties from Data Sets, Bayesian Design of Experiments in Chemical Kinetics**, ChemCatChem, 12, 21, 2020. Crossref

  12. Almohammadi Saja, Hantouche Mireille, Le Maître Olivier P., Knio Omar M., A tangent linear approximation of the ignition delay time. I: Sensitivity to rate parameters, Combustion and Flame, 230, 2021. Crossref

  13. Casey T.A., Khalil M., Najm H.N., Inference and combination of missing data sets for the determination of H2O2 thermal decomposition rate uncertainty, Combustion and Flame, 232, 2021. Crossref

  14. Horvatits Caitlin, Lee Jungkuk, Kyriakidou Eleni A., Walker Eric A., Characterizing Adsorption Sites on Ag/SSZ-13 Zeolites: Experimental Observations and Bayesian Inference, The Journal of Physical Chemistry C, 124, 35, 2020. Crossref

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