A full-discrete exponential Euler approximation of invariant measure for parabolic stochastic partial differential equations

Abstract

We discrete the ergodic semilinear stochastic partial differential equations in space dimension $d\le 3$ with additive noise, spatially by a spectral Galerkin method and temporally by an exponential Euler scheme. It is shown that both the spatial semi-discretization and the spatio-temporal full discretization are ergodic. Further, convergence orders of the numerical invariant measures, depending on the regularity of noise, are recovered based on an easy time-independent weak error analysis without relying on Malliavin calculus. To be precise, the convergence order is $1-\epsilon$ in space and $\frac{1}{2}-\epsilon$ in time for the space-time white noise case and $2-\epsilon$ in space and $1-\epsilon$ in time for the trace class noise case in space dimension $d=1$, with arbitrarily small $\epsilon >0$. Numerical results are finally reported to confirm these theoretical findings.

arXiv comment27 pages, to appear in: Applied Numerical Mathematics

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Cited by in corpus (5)

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• Numerical Ergodicity and Uniform Estimate of Monotone SPDEs Driven by Multiplicative Noise
• Dimension-free convergence rates for gradient Langevin dynamics in RKHS
• Finite-Volume approximation of the invariant measure of a viscous stochastic scalar conservation law
• Large deviations principles of sample paths and invariant measures of numerical methods for parabolic SPDEs