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首页> 外文期刊>Applied mathematics and optimization >Milstein Approximation for Advection-Diffusion Equations Driven by Multiplicative Noncontinuous Martingale Noises
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Milstein Approximation for Advection-Diffusion Equations Driven by Multiplicative Noncontinuous Martingale Noises

机译:非连续Martingale噪声驱动的对流扩散方程的Milstein逼近

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摘要

In this paper, the strong approximation of a stochastic partial differential equation, whose differential operator is of advection-diffusion type and which is driven by a multiplicative, infinite dimensional, càdlàg, square integrable martingale, is presented. A finite dimensional projection of the infinite dimensional equation, for example a Galerkin projection, with nonequidistant time stepping is used. Error estimates for the discretized equation are derived in L2 and almost sure senses. Besides space and time discretizations, noise approximations are also provided, where the Milstein double stochastic integral is approximated in such a way that the overall complexity is not increased compared to an Euler-Maruyama approximation. Finally, simulations complete the paper.
机译:本文提出了一个随机偏微分方程的强逼近,该方程的微分算子是对流扩散型,由一个乘积,无限维,平方可积à驱动。使用无限维方程的有限维投影,例如具有等距时间步长的Galerkin投影。离散方程的误差估计在L2和几乎确定的意义上得出。除了空间和时间离散以外,还提供了噪声近似,其中,与Euler-Maruyama近似相比,Milstein双随机积分的近似方式不会增加总体复杂度。最后,仿真完成了本文。

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