Strong convergence rates for nonlinearity-truncated Euler-type approximations of stochastic Ginzburg-Landau equations

Understand

This article proposes and analyzes explicit and easily implementable temporal numerical approximation schemes for additive noise-driven stochastic partial differential equations (SPDEs) with polynomial nonlinearities such as, e.g., stochastic Ginzburg-Landau equations.

• We prove essentially sharp strong convergence rates for the considered approximation schemes.
• Our analysis is carried out for abstract stochastic evolution equations on separable Banach and Hilbert spaces including the above mentioned SPDEs as special cases.
• We also illustrate our strong convergence rate results by means of a numerical simulation in Matlab.