Parareal algorithms for stochastic Maxwell equations with the damping term driven by additive noise

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Hauptverfasser: Zhang, Liying, Zhang, Qi
Format: Preprint
Veröffentlicht: 2024
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author Zhang, Liying
Zhang, Qi
author_facet Zhang, Liying
Zhang, Qi
contents In this paper, we investigate the strong convergence analysis of parareal algorithms for stochastic Maxwell equations with the damping term driven by additive noise. The proposed parareal algorithms proceed as two-level temporal parallelizable integrators with the stochastic exponential integrator as the coarse propagator and both the exact solution integrator and the stochastic exponential integrator as the fine propagator. It is proved that the convergence order of the proposed algorithms linearly depends on the iteration number. Numerical experiments are performed to illustrate the convergence of the parareal algorithms for different choices of the iteration number and the damping coefficient.
format Preprint
id arxiv_https___arxiv_org_abs_2407_10907
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Parareal algorithms for stochastic Maxwell equations with the damping term driven by additive noise
Zhang, Liying
Zhang, Qi
Numerical Analysis
In this paper, we investigate the strong convergence analysis of parareal algorithms for stochastic Maxwell equations with the damping term driven by additive noise. The proposed parareal algorithms proceed as two-level temporal parallelizable integrators with the stochastic exponential integrator as the coarse propagator and both the exact solution integrator and the stochastic exponential integrator as the fine propagator. It is proved that the convergence order of the proposed algorithms linearly depends on the iteration number. Numerical experiments are performed to illustrate the convergence of the parareal algorithms for different choices of the iteration number and the damping coefficient.
title Parareal algorithms for stochastic Maxwell equations with the damping term driven by additive noise
topic Numerical Analysis
url https://arxiv.org/abs/2407.10907