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arXiv 1809.04438math.NAcs.NA
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Magnus型积分器用于乘性噪声驱动的非自治半线性抛物型SPDE的有限元离散化

Magnus-type Integrator for the Finite Element Discretization of Semilinear Parabolic non-Autonomous SPDEs Driven by multiplicative noise

Western Norway University of Applied Sciences(西挪威应用科学大学);Center for Research in Computational and Applied Mechanics (CERECAM)(计算与应用力学研究中心);Department of Mathematics and Applied Mathematics, University of Cape Town(开普敦大学数学与应用数学系);The African Institute for Mathematical Sciences(AIMS) of South Africa(南非非洲数学科学研究院);Fakultät für Mathematik, Technische Universität Chemnitz(开姆尼茨工业大学数学学院)

Antoine Tambue, Jean Daniel Mukam

AI总结:

本文研究了由乘性噪声驱动的非自治半线性抛物型随机偏微分方程的数值近似,采用有限元方法和Magnus型积分器进行空间和时间离散,证明了在根均方L²范数下强收敛性,并分析了噪声和初始数据正则性对收敛阶的影响。

摘要:

本文旨在研究由乘性噪声驱动的一般二阶非自治半线性抛物型随机偏微分方程(SPDE)的数值近似。文献中对自治SPDE的数值近似已有深入研究,而非自治情况尚未完全理解。我们通过有限元方法在空间上、Magnus型积分器在时间上对非自治SPDE进行离散。我们证明了在根均方$L^2$范数下,全离散方案向弱解的强收敛性。结果揭示了空间和时间收敛阶如何依赖于噪声和初始数据的正则性。特别地,对于乘性迹类噪声,我们实现了收敛阶$\mathcal{O}\left(h^2\left(1+\max(0,\ln\left(t_m/h^2 ight) ight)+Δt^{1/2} ight)$。提供的数值模拟用于说明我们的理论发现。

英文摘要:

This paper aims to investigate numerical approximation of a general second order non-autonomous semilinear parabolic stochastic partial differential equation (SPDE) driven by multiplicative noise. Numerical approximations of autonomous SPDEs are thoroughly investigated in the literature, while the non-autonomous case is not yet understood. We discretize the non-autonomous SPDE driven by multiplicative noise by the finite element method in space and the Magnus-type integrator in time. We provide a strong convergence proof of the fully discrete scheme toward the mild solution in the root-mean-square $L^2$ norm. The result reveals how the convergence orders in both space and time depend on the regularity of the noise and the initial data. In particular, for multiplicative trace class noise we achieve convergence order $\mathcal{O}\left(h^2\left(1+\max(0,\ln\left(t_m/h^2\right)\right)+Δt^{1/2}\right)$. Numerical simulations to illustrate our theoretical finding are provided.

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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