Modeling uncertainties in molecular dynamics simulations using a stochastic reduced-order basis - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Computer Methods in Applied Mechanics and Engineering Année : 2019

Modeling uncertainties in molecular dynamics simulations using a stochastic reduced-order basis

Résumé

A methodology enabling the robust treatment of model-form uncertainties in molecular dynamics simulations is proposed. The approach consists in properly randomizing a reduced-order basis, obtained by the method of snapshots in the configuration space. A multi-step strategy to identify the hyperparameters in the stochastic reduced-order basis is further introduced. The relevance of the framework is finally demonstrated by characterizing various types of modeling errors associated with molecular dynamics simulations on a graphene sheet. In particular, the ability of the framework to represent uncertainties raised by model reduction and interatomic potential selection is assessed.
Fichier principal
Vignette du fichier
publi-2019-CMAME_wang-guilleminot-soize-preprint.pdf (4.91 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02146341 , version 1 (03-06-2019)

Identifiants

Citer

Haoran Wang, Johann Guilleminot, Christian Soize. Modeling uncertainties in molecular dynamics simulations using a stochastic reduced-order basis. Computer Methods in Applied Mechanics and Engineering, 2019, 354, pp.37-55. ⟨10.1016/j.cma.2019.05.020⟩. ⟨hal-02146341⟩
60 Consultations
251 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More