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Statistical inference for partial differential equations

Grenier, Emmanuel; Hoffmann, Marc; Lelièvre, Tony; Louvet, Violaine; Prieur, Clémentine; Rachdi, Nabil; Vigneaux, Paul (2014), Statistical inference for partial differential equations, ESAIM : Proceedings and surveys, sept.2014, vol. 45, EDP Sciences : Les Ulis, p. 178-188. 10.1051/proc/201445018

Type
Communication / Conférence
External document link
https://hal.inria.fr/hal-00920853
Date
2014
Book title
ESAIM : Proceedings and surveys, sept.2014, vol. 45
Publisher
EDP Sciences
Published in
Les Ulis
Pages
178-188
Publication identifier
Metadata
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Author(s)
Grenier, Emmanuel
Unité de Mathématiques Pures et Appliquées [UMPA-ENSL]
Hoffmann, Marc
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Lelièvre, Tony cc
Centre d'Enseignement et de Recherche en Mathématiques, Informatique et Calcul Scientifique [CERMICS]
Louvet, Violaine
Institut Camille Jordan [Villeurbanne] [ICJ]
Prieur, Clémentine
INRA [MOISE]
Rachdi, Nabil
ASTRIUM
Vigneaux, Paul cc
Unité de Mathématiques Pures et Appliquées [UMPA-ENSL]
Abstract (EN)
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.
Subjects / Keywords
PDEs