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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...
Autores principales: | , , |
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Lenguaje: | eng |
Publicado: |
Springer
2020
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.1007/978-3-030-48721-8 http://cds.cern.ch/record/2727040 |
_version_ | 1780966279925989376 |
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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. |
id | cern-2727040 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2020 |
publisher | Springer |
record_format | invenio |
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 AT rozzagianluigi quantificationofuncertaintyimprovingefficiencyandtechnologyquietselectedcontributions |