Pathwise Uniform Convergence of Time Discretisation Schemes for SPDEs
Abstract
In this paper, we prove convergence rates for time discretisation schemes for semi-linear stochastic evolution equations with additive or multiplicative Gaussian noise, where the leading operator A is the generator of a strongly continuous semigroup S on a Hilbert space X, and the focus is on non-parabolic problems. The main results are optimal bounds for the uniform strong error Ek∞ := (E j∈ \0, …, Nk\ \|U(tj) - Uj\|p)1/p, where p ∈ [2,∞), U is the mild solution, Uj is obtained from a time discretisation scheme, k is the step size, and Nk = T/k. The usual schemes such as the exponential Euler, the implicit Euler, and the Crank-Nicolson method, etc. are included as special cases. Under conditions on the nonlinearity and the noise, we show - Ek∞ k (T/k) (linear equation, additive noise, general S); - Ek∞ k (T/k) (nonlinear equation, multiplicative noise, contractive S); - Ek∞ k (T/k) (nonlinear wave equation, multiplicative noise) for a large class of time discretisation schemes. The logarithmic factor can be removed if the exponential Euler method is used with a (quasi)-contractive S. The obtained bounds coincide with the optimal bounds for SDEs. Most of the existing literature is concerned with bounds for the simpler pointwise strong error Ek:=(j∈ \0,…,Nk\E \|U(tj) - Uj\|p)1/p. Applications to Maxwell equations, Schr\"odinger equations, and wave equations are included. For these equations, our results improve and reprove several existing results with a unified method and provide the first results known for the implicit Euler and the Crank-Nicolson method.
Turn this paper into a lesson
ArcXiv compiles a structured reading guide from this paper's metadata: plain-English importance, contributions, prerequisite concepts, which sections to read first, flashcards, and a quiz. Grounded in the abstract, never invented.