Stochastic closure for partially-resolved turbulence - Archive ouverte HAL Accéder directement au contenu
Poster De Conférence Année : 2024

Stochastic closure for partially-resolved turbulence

Résumé

To better constraint and explain data-based algorithms (e.g., data assimilation, Bayesian inference, machine learning), we tie priors to physics. Although statistical physics propose stochastic processes to be used as priors, many real systems hardly accommodate with some usual statistical simplifications (e.g., stationarity, delta-correlation). Therefore, our framework stands halfway between analytic stochastic descriptions and heavy numerical simulations. We rely on turbulence multiscale decomposition and stochastic transport. This framework is applied to wave-turbulence interactions and model errors quantification for data assimilation in geophysical fluid mechanics and reduced order models.
Fichier principal
Vignette du fichier
Poster-Houches-2024.pdf (10.38 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04549011 , version 1 (16-04-2024)

Licence

Paternité

Identifiants

  • HAL Id : hal-04549011 , version 1

Citer

Valentin Resseguier, Chloé Caville, Merveille Talla, Antoine Moneyron, Guillaume Lepape, et al.. Stochastic closure for partially-resolved turbulence. Houches 2024 - Complex Systems, Statistical Mechanics and Machine Learning Crossover (in Memory of Giovanni Paladin), Mar 2024, Les Houches, France. pp.1-1, 2024. ⟨hal-04549011⟩
0 Consultations
0 Téléchargements

Partager

Gmail Facebook X LinkedIn More