A hybrid parareal Monte Carlo algorithm for parabolic problems - Université de Paris - Faculté des Sciences Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2022

A hybrid parareal Monte Carlo algorithm for parabolic problems

Résumé

In this work, we propose a hybrid Monte Carlo/deterministic “parareal-in- time” approach devoted to accelerating Monte Carlo simulations over massively parallel computing environments for the simulation of time-dependent problems. This parareal approach iterates on two different solvers: a low-cost “coarse” solver based on a very cheap deterministic Galerkin scheme and a “fine” solver based on a high-fidelity Monte Carlo resolution. In a set of benchmark numerical experiments based on a toy model con- cerning the time-dependent diffusion equation, we compare our hybrid parareal strategy with a standard full Monte Carlo solution. In particular, we show that for a large number of processors, our hybrid strategy significantly reduces the computational time of the simulation while preserving its accuracy. The conver- gence properties of the proposed Monte Carlo/deterministic parareal strategy are also discussed.
Fichier principal
Vignette du fichier
manuscript_revised_second.pdf (1.74 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03143554 , version 1 (16-02-2021)
hal-03143554 , version 2 (11-03-2021)
hal-03143554 , version 3 (23-06-2022)
hal-03143554 , version 4 (24-09-2022)
hal-03143554 , version 5 (11-10-2022)

Identifiants

  • HAL Id : hal-03143554 , version 3

Citer

Jad Dabaghi, Yvon Maday, Andrea Zoia. A hybrid parareal Monte Carlo algorithm for parabolic problems. 2022. ⟨hal-03143554v3⟩
252 Consultations
154 Téléchargements

Partager

Gmail Facebook X LinkedIn More