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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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Lenguaje: | eng |
Publicado: |
Elsevier Science
2015
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Acceso en línea: | http://cds.cern.ch/record/2121477 |
_version_ | 1780949374473338880 |
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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 |
id | cern-2121477 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2015 |
publisher | Elsevier Science |
record_format | invenio |
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 |