论文标题
快速在线自适应富集毛弹性，高对比度
Fast Online Adaptive Enrichment for Poroelasticity with High Contrast
论文作者
论文摘要
在这项工作中，我们在约束能量最小化通用多尺度有限元方法（CEM-GMSFEM）的框架内开发了一种在线自适应富集方法，用于求解具有高对比度系数的线性异质性毛弹性模型。所提出的方法利用残留驱动的误差指标的信息来丰富模型中位移和压力变量的多尺度空间。其他在线基础功能是相应地在超采样区域中构建的，并且可以自适应地选择以减少错误。通过彻底的数值实验提供了对在线富集算法的完整理论分析。
In this work, we develop an online adaptive enrichment method within the framework of the Constraint Energy Minimizing Generalized Multiscale Finite Element Method (CEM-GMsFEM) for solving the linear heterogeneous poroelasticity models with coefficients of high contrast. The proposed method makes use of information of residual-driven error indicators to enrich the multiscale spaces for both the displacement and the pressure variables in the model. Additional online basis functions are constructed in oversampled regions accordingly and are adaptively chosen to reduce the error the most. A complete theoretical analysis of the online enrichment algorithm is provided and justified by thorough numerical experiments.
