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Exponential moment bounds and strong convergence rates for tamed-truncated numerical approximations of stochastic convolutions
Arnulf Jentzen, Felix Lindner, Primož Pušnik · 2018-12-13 · via math.PR updates on arXiv.org

In this article we establish exponential moment bounds, moment bounds in fractional order smoothness spaces, a uniform Hölder continuity in time, and strong convergence rates for a class of fully discrete exponential Euler-type numerical approximations of infinite dimensional stochastic convolution processes. The considered approximations involve specific taming and truncation terms and are therefore well suited to be used in the context of SPDEs with non-globally Lipschitz continuous nonlinearities.

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