A low-rank solver for parameter estimation and uncertainty quantification in time-dependent systems of Partial Differential Equations - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Article Dans Une Revue Journal of Scientific Computing Année : 2024

A low-rank solver for parameter estimation and uncertainty quantification in time-dependent systems of Partial Differential Equations

Résumé

In this work we propose a low-rank solver in view of performing parameter estimation and uncertainty quantification in systems of Partial Differential Equations. The solution approximation is sought in a space-parameter separated form. The discretisation in the parameter direction is made evolve in time through a Markov Chain Monte Carlo method. The resulting method is a Bayesian sequential estimation of the parameters. The computational burden is mitigated by the introduction of an efficient interpolator, based on a reduced basis built by exploiting the low-rank solves. The method is tested on four different applications.
Fichier principal
Vignette du fichier
main.pdf (3.68 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03908682 , version 1 (20-12-2022)
hal-03908682 , version 2 (04-02-2024)

Licence

Paternité

Identifiants

  • HAL Id : hal-03908682 , version 1

Citer

Sébastien Riffaud, Miguel Angel Fernández, Damiano Lombardi. A low-rank solver for parameter estimation and uncertainty quantification in time-dependent systems of Partial Differential Equations. Journal of Scientific Computing, In press. ⟨hal-03908682v1⟩
157 Consultations
83 Téléchargements

Partager

Gmail Facebook X LinkedIn More