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 $$\mathrm{E}_{k}^{\infty} := \Big(\mathbb{E} \sup_{j\in \{0, \ldots, N_k\}} \|U(t_j) - U^j\|^p\Big)^{1/p},$$ where $p \in [2,\infty)$, $U$ is the mild solution, $U^j$ is obtained from a time discretisation scheme, $k$ is the step size, and $N_k = T/k$. The usual schemes such as splitting/exponential Euler, implicit Euler, and Crank-Nicolson, etc.\ are included as special cases. Under conditions on the nonlinearity and the noise we show - $\mathrm{E}_{k}^{\infty}\lesssim k \log(T/k)$ (linear equation, additive noise, general $S$); - $\mathrm{E}_{k}^{\infty}\lesssim \sqrt{k} \log(T/k)$ (nonlinear equation, multiplicative noise, contractive $S$); - $\mathrm{E}_{k}^{\infty}\lesssim k \log(T/k)$ (nonlinear wave equation, multiplicative noise). The logarithmic factor can be removed if the splitting scheme 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 $$\mathrm{E}_k:=\bigg(\sup_{j\in \{0,\ldots,N_k\}}\mathbb{E} \|U(t_j) - U^{j}\|^p\bigg)^{1/p}.$$ 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.
翻译:在本文中,我们证明了针对具有加性或乘性高斯噪声的半线性随机演化方程的时间离散方案的收敛速度,其中领先算子 $A$ 是希尔伯特空间 $X$ 上强连续半群 $S$ 的生成器,并侧重于非抛物问题。主要结果是均匀强误差$$\mathrm{E}_{k}^{\infty} := \Big(\mathbb{E} \sup_{j\in \{0, \ldots, N_k\}} \|U(t_j) - U^j\|^p\Big)^{1/p},$$其中 $p \in [2,\infty)$,$U$ 是温和解,$U^j$ 是从时间离散化方案中获得的,$k$ 是步长,$N_k = T/k$。通常的方案,如分裂/指数欧拉,隐式欧拉和克兰-尼科尔森等,都包括在特殊情况下。在非线性和噪声方面的条件下,我们展示了 - $\mathrm{E}_{k}^{\infty}\lesssim k \log(T/k)$(线性方程,加性噪声,一般 $S$); - $\mathrm{E}_{k}^{\infty}\lesssim \sqrt{k} \log(T/k)$(非线性方程,乘性噪声,收缩的 $S$); - $\mathrm{E}_{k}^{\infty}\lesssim k \log(T/k)$(非线性波动方程,乘性噪声)。如果使用分裂方案与(准)收缩的 $S$,就可以消除对数因子。得到的界与 SDE 的最优界相符。现有文献大多关注较简单的点态强误差$$\mathrm{E}_k:=\bigg(\sup_{j\in \{0,\ldots,N_k\}}\mathbb{E} \|U(t_j) - U^{j}\|^p\bigg)^{1/p}.$$各种方程的应用均予考虑,包括麦克斯韦方程、薛定谔方程和波动方程。对于这些方程,我们的结果用统一的方法改进并证明了现有的多项结果。