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A MULTIGRID MULTILEVEL MONTE CARLO METHOD USING HIGH-ORDER FINITE-VOLUME SCHEME FOR LOGNORMAL DIFFUSION PROBLEMS

机译:对数扩散问题的高阶有限体积方案的多重网格多级蒙特卡罗方法

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摘要

The aim of this paper is to show that a high-order discretization can be used to improve the convergence of a multilevel Monte Carlo method for elliptic partial differential equations with lognormal random coefficients in combination with the multigrid solution method. To demonstrate this, we consider a fourth-order accurate finite-volume discretization. With the help of the Matern family of covariance functions, we simulate the coefficient field with different degrees of smoothness. The idea behind using a fourth-order scheme is to capture the additional regularity in the solution introduced due to higher smoothness of the random field. Second-order schemes previously utilized for these types of problems are not able to fully exploit this additional regularity. We also propose a practical way of combining a full multigrid solver with the multilevel Monte Carlo estimator constructed on the same mesh hierarchy. Through this integration, one full multigrid solve at any level provides a valid sample for all the preceding Monte Carlo levels. The numerical results show that the fourth-order multilevel estimator consistently outperforms the second-order variant. In addition, we observe an asymptotic gain for the standard Monte Carlo estimator.
机译:本文的目的是证明高阶离散化可用于改进对数正态随机系数的椭圆偏微分方程的多级蒙特卡罗方法与多网格求解方法的收敛性。为了证明这一点,我们考虑了四阶精确的有限体积离散化。借助Matern系列协方差函数,我们可以模拟具有不同平滑度的系数场。使用四阶方案背后的想法是,由于随机场的更高平滑度,在引入的解决方案中捕获了额外的规律性。以前用于这些类型问题的二阶方案不能完全利用这种额外的规律性。我们还提出了一种将完整的多网格求解器与在相同网格层次上构建的多级蒙特卡洛估计器相结合的实用方法。通过这种集成,任何级别的完整多网格求解都可以为所有先前的蒙特卡洛级别提供有效的样本。数值结果表明,四阶多级估计量始终优于二阶变量。另外,我们观察到标准蒙特卡洛估计器的渐近增益。

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