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Quantification of uncertainty improving efficiency and technology: quiet selected contributions

This book explores four guiding themes – reduced order modelling, high dimensional problems, efficient algorithms, and applications – by reviewing recent algorithmic and mathematical advances and the development of new research directions for uncertainty quantification in the context of partial diff...

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Detalles Bibliográficos
Autores principales: D'Elia, Marta, Gunzburger, Max, Rozza, Gianluigi
Lenguaje:eng
Publicado: Springer 2020
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-3-030-48721-8
http://cds.cern.ch/record/2727040
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author D'Elia, Marta
Gunzburger, Max
Rozza, Gianluigi
author_facet D'Elia, Marta
Gunzburger, Max
Rozza, Gianluigi
author_sort D'Elia, Marta
collection CERN
description This book explores four guiding themes – reduced order modelling, high dimensional problems, efficient algorithms, and applications – by reviewing recent algorithmic and mathematical advances and the development of new research directions for uncertainty quantification in the context of partial differential equations with random inputs. Highlighting the most promising approaches for (near-) future improvements in the way uncertainty quantification problems in the partial differential equation setting are solved, and gathering contributions by leading international experts, the book’s content will impact the scientific, engineering, financial, economic, environmental, social, and commercial sectors.
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institution Organización Europea para la Investigación Nuclear
language eng
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spelling cern-27270402021-04-21T18:05:34Zdoi:10.1007/978-3-030-48721-8http://cds.cern.ch/record/2727040engD'Elia, MartaGunzburger, MaxRozza, GianluigiQuantification of uncertainty improving efficiency and technology: quiet selected contributionsMathematical Physics and MathematicsThis book explores four guiding themes – reduced order modelling, high dimensional problems, efficient algorithms, and applications – by reviewing recent algorithmic and mathematical advances and the development of new research directions for uncertainty quantification in the context of partial differential equations with random inputs. Highlighting the most promising approaches for (near-) future improvements in the way uncertainty quantification problems in the partial differential equation setting are solved, and gathering contributions by leading international experts, the book’s content will impact the scientific, engineering, financial, economic, environmental, social, and commercial sectors.Springeroai:cds.cern.ch:27270402020
spellingShingle Mathematical Physics and Mathematics
D'Elia, Marta
Gunzburger, Max
Rozza, Gianluigi
Quantification of uncertainty improving efficiency and technology: quiet selected contributions
title Quantification of uncertainty improving efficiency and technology: quiet selected contributions
title_full Quantification of uncertainty improving efficiency and technology: quiet selected contributions
title_fullStr Quantification of uncertainty improving efficiency and technology: quiet selected contributions
title_full_unstemmed Quantification of uncertainty improving efficiency and technology: quiet selected contributions
title_short Quantification of uncertainty improving efficiency and technology: quiet selected contributions
title_sort quantification of uncertainty improving efficiency and technology: quiet selected contributions
topic Mathematical Physics and Mathematics
url https://dx.doi.org/10.1007/978-3-030-48721-8
http://cds.cern.ch/record/2727040
work_keys_str_mv AT deliamarta quantificationofuncertaintyimprovingefficiencyandtechnologyquietselectedcontributions
AT gunzburgermax quantificationofuncertaintyimprovingefficiencyandtechnologyquietselectedcontributions
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