We study a class of fully-discrete schemes for the numerical approximation of solutions of stochastic Cahn--Hilliard equations with cubic nonlinearity and driven by additive noise. The spatial (resp. temporal) discretization is performed with a spectral Galerkin method (resp. a tamed exponential Euler method). We consider two situations: space-time white noise in dimension $d=1$ and trace-class noise in dimensions $d=1,2,3$. In both situations, we prove weak error estimates, where the weak order of convergence is twice the strong order of convergence with respect to the spatial and temporal discretization parameters. To prove these results, we show appropriate regularity estimates for solutions of the Kolmogorov equation associated with the stochastic Cahn--Hilliard equation, which have not been established previously and may be of interest in other contexts.
翻译:我们研究的是一组完全分解的系统,以便用不线性立方体和添加性噪声驱动的随机卡赫恩-希利亚德方程式的解决方案数字近似值,空间(时间)分解采用光谱加勒金法(调制指数极速法)进行。我们考虑了两种情况:空间时白噪音在维度上=1美元,微量级噪音在维度上=1美元=1美元,微量级噪音在维度=1美元3美元。在这两种情况下,我们证明误差估计是微弱的,在空间和时间分解参数上,衰弱的趋同顺序是高度趋同的两倍。为了证明这些结果,我们为与以前没有确定的、其他情况下可能感兴趣的高孔-希利亚德方程式相关的科尔莫戈夫方程式的解决方案提供了适当的定期估计值。