In this paper, we propose an offline-online strategy based on the Localized Orthogonal Decomposition (LOD) method for elliptic multiscale problems with randomly perturbed diffusion coefficient. We consider a periodic deterministic coefficient with local defects that occur with probability $p$. The offline phase pre-computes entries to global LOD stiffness matrices on a single reference element (exploiting the periodicity) for a selection of defect configurations. Given a sample of the perturbed diffusion the corresponding LOD stiffness matrix is then computed by taking linear combinations of the pre-computed entries, in the online phase. Our computable error estimates show that this yields a good coarse-scale approximation of the solution for small $p$. Moreover, extensive numerical experiments illustrate that relative errors of a few percent are achieved up to at least $p=0.1$. This makes the proposed technique attractive already for moderate sample sizes in a Monte Carlo simulation.
翻译:在本文中,我们提出一个离线战略,其依据是用于随机扰动扩散系数的椭圆形多尺度问题局部性分解法(LOD)方法。我们考虑一个周期性确定系数,其局部缺陷发生概率为$p美元。离线阶段预先计算了一个单一参考元素(利用周期)的全球LOD硬度矩阵条目,用于选择缺陷配置。根据受扰散扩散的样本,随后在网上阶段采用预先计算条目的线性组合来计算相应的LOD硬度矩阵。我们的计算误差估计表明,这产生一个小美元解决方案的粗略近似值。此外,广泛的数字实验表明,少数比例的相对差值达到至少$p=0.1美元。这使得拟议的技术在蒙特卡洛模拟中已经吸引中度样本大小。