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An iterative adaptive finite element method for elliptic eigenvalue problems

机译:椭圆特征值问题的迭代自适应有限元方法

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We consider the task of resolving accurately the nth eigenpair of a generalized eigenproblem rooted in some elliptic partial differential equation (PDE), using an adaptive finite element method (FEM). Conventional adaptive FEM algorithms call a generalized eigensolver after each mesh refinement step. This is not practical in our situation since the generalized eigensolver needs to calculate n eigenpairs after each mesh refinement step, it can switch the order of eigenpairs, and for repeated eigenvalues it can return an arbitrary linear combination of eigenfunctions from the corresponding eigenspace. In order to circumvent these problems, we propose a novel adaptive algorithm that only calls a generalized eigensolver once at the beginning of the computation, and then employs an iterative method to pursue a selected eigenvalue-eigenfunction pair on a sequence of locally refined meshes. Both Picard's and Newton's variants of the iterative method are presented. The underlying partial differential equation (PDE) is discretized with higher-order finite elements (hp-FEM) but the algorithm also works for standard low-order FEM. The method is described and accompanied with theoretical analysis and numerical examples. Instructions on how to reproduce the results are provided.
机译:我们考虑使用自适应有限元方法(FEM)准确解决根于某些椭圆型偏微分方程(PDE)的广义本征问题的第n个本征对的任务。在每个网格细化步骤之后,传统的自适应FEM算法都会调用广义特征求解器。这在我们的情况下不切实际,因为广义特征求解器需要在每个网格细化步骤之后计算n个特征对,它可以切换特征对的顺序,并且对于重复的特征值,它可以从相应的特征空间返回特征函数的任意线性组合。为了解决这些问题,我们提出了一种新颖的自适应算法,该算法在计算开始时仅调用一次广义特征求解器,然后采用迭代方法在局部精炼网格序列上追求选定的特征值-特征函数对。同时介绍了Picard和Newton的迭代方法。底层偏微分方程(PDE)通过高阶有限元(hp-FEM)离散化,但是该算法也适用于标准低阶FEM。描述了该方法,并附带了理论分析和数值示例。提供了有关如何重现结果的说明。

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