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dc.contributor.authorVigneaux, Paul
HAL ID: 5729
ORCID: 0000-0001-6606-5446
dc.contributor.authorRachdi, Nabil
dc.contributor.authorPrieur, Clémentine
dc.contributor.authorLouvet, Violaine
dc.contributor.authorLelièvre, Tony
HAL ID: 170972
ORCID: 0000-0002-3412-113X
dc.contributor.authorHoffmann, Marc
dc.contributor.authorGrenier, Emmanuel
dc.subjectPartial Differential Equationsen
dc.titleParameter Estimation For Partial Differential Equationsen
dc.typeDocument de travail / Working paper
dc.contributor.editoruniversityotherGdR MASCOT-NUM ((Méthodes d'Analyse Stochastique des Codes et Traitements Numériques));France
dc.contributor.editoruniversityotherMOISE (INRIA Grenoble Rhône-Alpes / LJK Laboratoire Jean Kuntzmann);France
dc.contributor.editoruniversityotherInstitut Camille Jordan (ICJ);France
dc.contributor.editoruniversityotherCentre d'Enseignement et de Recherche en Mathématiques et Calcul Scientifique (CERMICS);France
dc.contributor.editoruniversityotherNUMED (ENS Lyon / UCB Lyon / Inria Grenoble Rhône-Alpes);France
dc.description.abstractenRésumé : 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.en
dc.publisher.nameUniversité Paris-Dauphineen
dc.subject.ddclabelProbabilités et mathématiques appliquéesen

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