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Stochastic Galerkin techniques for random ordinary differential equations

机译:随机常微分方程的随机Galerkin技术

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Over the last decade the stochastic Galerkin method has become an established method to solve differential equations involving uncertain parameters. It is based on the generalized Wiener expansion of square integrable random variables. Although there exist very sophisticated variants of the stochastic Galerkin method (wavelet basis, multi-element approach) convergence for random ordinary differential equations has rarely been considered analytically. In this work we develop an asymptotic upper boundary for the L_2-error of the stochastic Galerkin method. Furthermore, we prove convergence of a local application of the stochastic Galerkin method and confirm convergence of the multi-element approach within this context.
机译:在过去的十年中,随机Galerkin方法已成为解决涉及不确定参数的微分方程的一种既定方法。它基于平方可积随机变量的广义Wiener展开。尽管存在随机Galerkin方法(小波基,多元素方法)的非常复杂的变体,但很少会分析地考虑随机常微分方程的收敛性。在这项工作中,我们为随机Galerkin方法的L_2误差开发了一个渐近上界。此外,我们证明了随机Galerkin方法在本地的收敛性,并在此背景下确认了多元素方法的收敛性。

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