We study the numerical approximation of stochastic evolution equations with a monotone drift driven by an infinite-dimensional Wiener process. To discretize the equation, we combine a drift-implicit two-step BDF method for the temporal discretization with an abstract Galerkin method for the spatial discretization. After proving well-posedness of the BDF2-Maruyama scheme, we establish a convergence rate of the strong error for equations under suitable Lipschitz conditions. We illustrate our theoretical results through various numerical experiments and compare the performance of the BDF2-Maruyama scheme to the backward Euler--Maruyama scheme.
翻译:我们用由无限维纳进程驱动的单质漂移来研究随机进化方程的数值近似值。 为了分解该方程, 我们将时间分解的隐含漂浮二步的BDF方法与空间分解的抽象Galerkin方法结合起来。 在证明BDF2- Maruyama 方案具有充分证据之后, 我们为合适的Lipschitz 条件下的方程设定了强烈误差的趋同率。 我们通过各种数字实验来说明我们的理论结果, 并将BDF2- Maruyama方案的绩效与落后的Euler- Maruyama 方案进行比较 。