Strong convergence of a fully discrete finite element method for a class of semilinear stochastic partial differential equations with multiplicative noise
Xiaobing Feng, Yukun Li, Yi Zhang

TL;DR
This paper introduces a fully discrete finite element method for semilinear SPDEs with multiplicative noise, proving strong convergence with nearly optimal rates despite complex nonlinearities and stability challenges.
Contribution
It develops a novel finite element discretization scheme for semilinear SPDEs with multiplicative noise and establishes its strong convergence with optimal rates.
Findings
Proposed a finite element scheme with strong convergence proof.
Achieved stability estimates for higher moments of the numerical solution.
Demonstrated nearly optimal convergence rates for the method.
Abstract
This paper develops and analyzes a fully discrete finite element method for a class of semilinear stochastic partial differential equations (SPDEs) with multiplicative noise. The nonlinearity in the diffusion term of the SPDEs is assumed to be globally Lipschitz and the nonlinearity in the drift term is only assumed to satisfy a one-side Lipschitz condition. The semilinear SPDEs considered in this paper is a direct generalization of the SODEs considered in [13]. There are several difficulties which need to be overcome for this generalization. First, obviously the spatial discretization, which does not appear in the SODE case, adds an extra layer of difficulty. It turns out a special discretization must be designed to guarantee certain properties for the numerical scheme and its stiffness matrix. In this paper we use a finite element interpolation technique to discretize the nonlinear…
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Taxonomy
TopicsStochastic processes and financial applications · Fluid Dynamics and Turbulent Flows · Advanced Numerical Methods in Computational Mathematics
