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arXiv 2209.14740math.NAcs.NA
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波数含不确定性的亥姆霍兹方程

The Helmholtz equation with uncertainties in the wavenumber

Institute of Mathematics and Computer Science, Universität Greifswald(格赖夫斯瓦尔德大学数学与计算机科学研究所)

Roland Pulch, Olivier Sète

AI总结:

针对波数含不确定性的亥姆霍兹方程,采用空间有限差分离散结合随机Galerkin方法得到高维复对称线性系统,提出两种预处理器提升GMRES迭代求解效率。

摘要:

我们研究具有合适边界条件且波数含不确定性的Helmholtz方程,为此将波数建模为随机变量或随机场。我们采用空间有限差分离散该Helmholtz方程,得到含随机变量的线性代数方程组。通过随机Galerkin方法推导出确定性的线性代数方程组,该系统高维、稀疏且复对称,但通常不具备厄米性。因此我们使用GMRES迭代求解该系统,并提出两种预处理器:复移位拉普拉斯预处理器和均值预处理器。数值实验表明,两种预处理器均能减少迭代步数并缩短计算时间。

英文摘要:

We investigate the Helmholtz equation with suitable boundary conditions and uncertainties in the wavenumber. Thus the wavenumber is modeled as a random variable or a random field. We discretize the Helmholtz equation using finite differences in space, which leads to a linear system of algebraic equations including random variables. A stochastic Galerkin method yields a deterministic linear system of algebraic equations. This linear system is high-dimensional, sparse and complex symmetric but, in general, not hermitian. We therefore solve this system iteratively with GMRES and propose two preconditioners: a complex shifted Laplace preconditioner and a mean value preconditioner. Both preconditioners reduce the number of iteration steps as well as the computation time in our numerical experiments.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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