Weak convergence of Galerkin approximations of stochastic partial differential equations driven by additive Lévy noise

Abstract

This work considers weak approximations of stochastic partial differential equations (SPDEs) driven by Lévy noise. The SPDEs at hand are parabolic with additive noise processes. A weak-convergence rate for the corresponding Galerkin approximation is derived. The convergence result is derived by use of the Malliavin derivative rather then the common approach via the Kolmogorov backward equation.

References in corpus (1)

• Weak error analysis for semilinear stochastic Volterra equations with additive noise

Cited by in corpus (1)

• Poisson Malliavin calculus in Hilbert space with an application to SPDE