Statistical inference for partial differential equations*
Many physical phenomena are modeled by parametrized PDEs. The poor knowledge on the involved parameters is often one of the numerous sources of uncertainties on these models. Some of these parameters can be estimated, with the use of real world data. The aim of this min...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2014-09-01
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| Series: | ESAIM: Proceedings and Surveys |
| Online Access: | http://dx.doi.org/10.1051/proc/201445018 |
| _version_ | 1797967127022403584 |
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| author | Grenier Emmanuel Hoffmann Marc Lelièvre Tony Louvet Violaine Prieur Clémentine Rachdi Nabil Vigneaux Paul |
| author_facet | Grenier Emmanuel Hoffmann Marc Lelièvre Tony Louvet Violaine Prieur Clémentine Rachdi Nabil Vigneaux Paul |
| author_sort | Grenier Emmanuel |
| collection | DOAJ |
| description | Many physical phenomena are modeled by parametrized PDEs. The poor knowledge on the
involved parameters is often one of the numerous sources of uncertainties on these models.
Some of these parameters can be estimated, with the use of real world data. The aim of
this mini-symposium is to introduce some of the various tools from both statistical and
numerical communities to deal with this issue. Parametric and non-parametric approaches
are developed in this paper. Some of the estimation procedures require many evaluations of
the initial model. Some interpolation tools and some greedy algorithms for model reduction
are therefore also presented, in order to reduce time needed for running the model. |
| first_indexed | 2024-04-11T02:25:13Z |
| format | Article |
| id | doaj.art-cea7b6d069e7454ebb1ef9358c24709f |
| institution | Directory Open Access Journal |
| issn | 2267-3059 |
| language | English |
| last_indexed | 2024-04-11T02:25:13Z |
| publishDate | 2014-09-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ESAIM: Proceedings and Surveys |
| spelling | doaj.art-cea7b6d069e7454ebb1ef9358c24709f2023-01-02T22:52:18ZengEDP SciencesESAIM: Proceedings and Surveys2267-30592014-09-014517818810.1051/proc/201445018proc144518Statistical inference for partial differential equations*Grenier Emmanuel0Hoffmann Marc1Lelièvre Tony2Louvet Violaine3Prieur Clémentine4Rachdi NabilVigneaux Paul5ENS Lyon, UPMA; INRIA, Project-team NUMEDParis Dauphine, CEREMADEUniversité Paris-Est, ENPC, CERMICS; INRIA, Project-team MICMACUniversité de Lyon, ICJ; INRIA, Project-team NUMEDUniversité Grenoble Alpes, LJK; INRIA, Project-team MOISEENS Lyon, UPMA; INRIA, Project-team NUMEDMany physical phenomena are modeled by parametrized PDEs. The poor knowledge on the involved parameters is often one of the numerous sources of uncertainties on these models. Some of these parameters can be estimated, with the use of real world data. The aim of this mini-symposium is to introduce some of the various tools from both statistical and numerical communities to deal with this issue. Parametric and non-parametric approaches are developed in this paper. Some of the estimation procedures require many evaluations of the initial model. Some interpolation tools and some greedy algorithms for model reduction are therefore also presented, in order to reduce time needed for running the model.http://dx.doi.org/10.1051/proc/201445018 |
| spellingShingle | Grenier Emmanuel Hoffmann Marc Lelièvre Tony Louvet Violaine Prieur Clémentine Rachdi Nabil Vigneaux Paul Statistical inference for partial differential equations* ESAIM: Proceedings and Surveys |
| title | Statistical inference for partial differential equations* |
| title_full | Statistical inference for partial differential equations* |
| title_fullStr | Statistical inference for partial differential equations* |
| title_full_unstemmed | Statistical inference for partial differential equations* |
| title_short | Statistical inference for partial differential equations* |
| title_sort | statistical inference for partial differential equations |
| url | http://dx.doi.org/10.1051/proc/201445018 |
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