Localized orthogonal decomposition for a multiscale parabolic stochastic partial differential equation
Artikel i vetenskaplig tidskrift, 2024

A multiscale method is proposed for a parabolic stochastic partial differential equation with additive noise and highly oscillatory diffusion. The framework is based on the localized orthogonal decomposition (LOD) method and computes a coarse-scale representation of the elliptic operator, enriched by fine-scale information on the diffusion. Optimal order strong convergence is derived. The LOD technique is combined with a (multilevel) Monte-Carlo estimator and the weak error is analyzed. Numerical examples that confirm the theoretical findings are provided, and the computational efficiency of the method is highlighted.

Författare

Annika Lang

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Per Ljung

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Axel Målqvist

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Multiscale Modeling and Simulation

1540-3459 (ISSN) 15403467 (eISSN)

Vol. 22 1 205-229

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Ämneskategorier

Beräkningsmatematik

Matematisk analys

DOI

10.1137/23M1569216

Mer information

Senast uppdaterat

2024-07-30