An averaged space-time discretization of the stochastic $p$-Laplace system

Abstract

We study the stochastic $p$-Laplace system in a bounded domain. We propose two new space-time discretizations based on the approximation of time-averaged values. We establish linear convergence in space and $1/2$ convergence in time. Additionally, we provide a sampling algorithm to construct the necessary random input in an efficient way. The theoretical error analysis is complemented by numerical experiments.

40 pages, 3 figures

References in corpus (4)

• SPDE in Hilbert Space with Locally Monotone Coefficients
• Rate of Convergence of Space Time Approximations for stochastic evolution equations
• Taming singular stochastic differential equations: A numerical method
• Improved error estimates for Hybrid High-Order discretizations of Leray-Lions problems

Cited by in corpus (2)

• Pathwise Uniform Convergence of Time Discretisation Schemes for SPDEs
• A class of space-time discretizations for the stochastic $p$-Stokes system