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Uncertainty quantification and stochastic modeling with Matlab

Uncertainty Quantification (UQ) is a relatively new research area which describes the methods and approaches used to supply quantitative descriptions of the effects of uncertainty, variability and errors in simulation problems and models. It is rapidly becoming a field of increasing importance, with...

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Detalles Bibliográficos
Autores principales: Souza de Cursi, Eduardo, Sampaio, Rubens
Lenguaje:eng
Publicado: Elsevier Science 2015
Materias:
Acceso en línea:http://cds.cern.ch/record/2121477
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author Souza de Cursi, Eduardo
Sampaio, Rubens
author_facet Souza de Cursi, Eduardo
Sampaio, Rubens
author_sort Souza de Cursi, Eduardo
collection CERN
description Uncertainty Quantification (UQ) is a relatively new research area which describes the methods and approaches used to supply quantitative descriptions of the effects of uncertainty, variability and errors in simulation problems and models. It is rapidly becoming a field of increasing importance, with many real-world applications within statistics, mathematics, probability and engineering, but also within the natural sciences. Literature on the topic has up until now been largely based on polynomial chaos, which raises difficulties when considering different types of approximation and does no
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institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2015
publisher Elsevier Science
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spelling cern-21214772021-04-21T19:55:12Zhttp://cds.cern.ch/record/2121477engSouza de Cursi, EduardoSampaio, RubensUncertainty quantification and stochastic modeling with MatlabMathematical Physics and MathematicsUncertainty Quantification (UQ) is a relatively new research area which describes the methods and approaches used to supply quantitative descriptions of the effects of uncertainty, variability and errors in simulation problems and models. It is rapidly becoming a field of increasing importance, with many real-world applications within statistics, mathematics, probability and engineering, but also within the natural sciences. Literature on the topic has up until now been largely based on polynomial chaos, which raises difficulties when considering different types of approximation and does noElsevier Scienceoai:cds.cern.ch:21214772015
spellingShingle Mathematical Physics and Mathematics
Souza de Cursi, Eduardo
Sampaio, Rubens
Uncertainty quantification and stochastic modeling with Matlab
title Uncertainty quantification and stochastic modeling with Matlab
title_full Uncertainty quantification and stochastic modeling with Matlab
title_fullStr Uncertainty quantification and stochastic modeling with Matlab
title_full_unstemmed Uncertainty quantification and stochastic modeling with Matlab
title_short Uncertainty quantification and stochastic modeling with Matlab
title_sort uncertainty quantification and stochastic modeling with matlab
topic Mathematical Physics and Mathematics
url http://cds.cern.ch/record/2121477
work_keys_str_mv AT souzadecursieduardo uncertaintyquantificationandstochasticmodelingwithmatlab
AT sampaiorubens uncertaintyquantificationandstochasticmodelingwithmatlab