A linearly implicit finite element full-discretization scheme for SPDEs with nonglobally Lipschitz coefficients

IF 2.3 2区 数学 Q1 MATHEMATICS, APPLIED
Mengchao Wang, Xiaojie Wang
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引用次数: 0

Abstract

The present article deals with strong approximations of additive noise driven stochastic partial differential equations (SPDEs) with nonglobally Lipschitz nonlinearity in a bounded domain $ \mathcal{D} \in{\mathbb{R}}^{d}$, $ d \leq 3$. As the first contribution, we establish the well-posedness and regularity of the considered SPDEs in space dimension $d \le 3$, under more relaxed assumptions on the stochastic convolution. This improves relevant results in the literature and covers both the space-time white noise ($d=1$) and the trace-class noises ($\text{Tr} (Q) < \infty $) in multiple dimensions $d=2,3$. Such an improvement is achieved based on a key perturbation estimate for a perturbed PDE, with the aid of which we prove the convergence and uniform regularity of a spectral approximation of the SPDEs and thus get the improved regularity results. The second contribution of the paper is to propose and analyze a spatio-temporal discretization of the SPDEs, by incorporating a standard finite element method in space and a linearly implicit nonlinearity-tamed Euler method for the temporal discretization. The proposed time-stepping scheme is linearly implicit and does not suffer from solving nonlinear algebra equations as the backward Euler scheme does. Based on the improved regularity results, we recover the expected strong convergence rates of the fully discrete scheme and reveal how the convergence rates rely on the regularity of the noise process. In particular, a classical convergence rate of order $O(h^{2} +\tau )$ can be obtained even in high dimension $d=3$, as the driven noise is of trace class and satisfies certain regularity assumptions. The optimal error estimates turn out to be challenging and face some essential difficulties when the tamed time-stepping scheme meets the finite element spatial discretization, particularly in the context of low regularity and multiple dimensions $d \le 3$. Some highly nontrivial arguments are introduced to overcome the difficulties. Finally, numerical examples corroborate the claimed strong orders of convergence.
非全局 Lipschitz 系数 SPDE 的线性隐式有限元全离散化方案
本文讨论了在有界域 $ \mathcal{D} 中具有非全局 Lipschitz 非线性的加性噪声驱动随机偏微分方程(SPDEs)的强近似。\in{mathbb{R}}^{d}$, $ d \leq 3$。作为第一个贡献,我们在随机卷积的更宽松假设下,建立了所考虑的 SPDE 在空间维度 $d \le 3$ 中的良好拟合性和正则性。这改进了文献中的相关结果,涵盖了多维度 $d=2,3$ 的时空白噪声($d=1$)和迹类噪声($\text{Tr} (Q) < \infty$)。这种改进是基于对受扰动 PDE 的关键扰动估计实现的,借助这种估计,我们证明了 SPDE 的谱近似的收敛性和均匀正则性,从而得到了改进的正则性结果。本文的第二个贡献是提出并分析了 SPDE 的时空离散方法,即在空间离散中采用标准有限元方法,在时间离散中采用线性隐式非线性驯服欧拉方法。所提出的时间步进方案是线性隐式的,不会像后向欧拉方案那样受非线性代数方程求解的影响。基于改进的正则性结果,我们恢复了完全离散方案的预期强收敛率,并揭示了收敛率如何依赖于噪声过程的正则性。特别是,即使在高维度 $d=3$ 的情况下,也能获得阶数为 $O(h^{2}+\tau)$的经典收敛率,因为驱动噪声属于迹类并满足某些正则性假设。当驯服的时间步进方案遇到有限元空间离散化时,特别是在低正则性和多维度 $d \le 3$ 的情况下,最优误差估计具有挑战性并面临一些基本困难。为了克服这些困难,我们引入了一些非难论证。最后,数值实例证实了所宣称的强收敛阶数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IMA Journal of Numerical Analysis
IMA Journal of Numerical Analysis 数学-应用数学
CiteScore
5.30
自引率
4.80%
发文量
79
审稿时长
6-12 weeks
期刊介绍: The IMA Journal of Numerical Analysis (IMAJNA) publishes original contributions to all fields of numerical analysis; articles will be accepted which treat the theory, development or use of practical algorithms and interactions between these aspects. Occasional survey articles are also published.
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