Strong convergence rates for explicit space-time discrete numerical approximations of stochastic Allen-Cahn equations
For researchers in numerical analysis of SPDEs, this fills a long-standing gap by providing rigorous convergence rates for a class of equations with superlinear nonlinearities.
This paper proves the first strong convergence rates for full-discrete numerical approximations of stochastic Allen-Cahn equations driven by space-time white noise, and establishes lower bounds showing these rates are essentially sharp.
The scientific literature contains a number of numerical approximation results for stochastic partial differential equations (SPDEs) with superlinearly growing nonlinearities but, to the best of our knowledge, none of them prove strong or weak convergence rates for full-discrete numerical approximations of space-time white noise driven SPDEs with superlinearly growing nonlinearities. In particular, in the scientific literature there exists neither a result which proves strong convergence rates nor a result which proves weak convergence rates for full-discrete numerical approximations of stochastic Allen-Cahn equations. In this article we bridge this gap and establish strong convergence rates for full-discrete numerical approximations of space-time white noise driven SPDEs with superlinearly growing nonlinearities such as stochastic Allen-Cahn equations. Moreover, we also establish lower bounds for strong temporal and spatial approximation errors which demonstrate that our strong convergence rates are essentially sharp and can, in general, not be improved.