莱维-福克-普朗克方程渐近保留方案的均匀误差估计

IF 2.2 2区 数学 Q1 MATHEMATICS, APPLIED
Weiran Sun, Li Wang
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引用次数: 0

摘要

我们为 Xu 和 Wang [Commun. Math. Sci. 21 (2023), pp.主要困难不仅来自比例参数和数值参数之间的相互作用,还来自平衡态尾部的缓慢衰减。我们根据缩放参数 ε \varepsilon 的相对大小来分离参数域,从而解决这些问题:在 ε \varepsilon 较大的情况下,我们设计了一种加权规范来缓解肥尾引起的问题;而在 ε \varepsilon 较小的情况下,我们证明了 LFP 向其分数扩散极限的强收敛性,并给出了明确的收敛速率。这种方法将传统的 AP 估计扩展到了无法获得均匀边界的情况。我们的结果适用于任何维度和分数幂的整个跨度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Uniform error estimate of an asymptotic preserving scheme for the Lévy-Fokker-Planck equation

We establish a uniform-in-scaling error estimate for the asymptotic preserving (AP) scheme proposed by Xu and Wang [Commun. Math. Sci. 21 (2023), pp. 1–23] for the Lévy-Fokker-Planck (LFP) equation. The main difficulties stem not only from the interplay between the scaling and numerical parameters but also the slow decay of the tail of the equilibrium state. We tackle these problems by separating the parameter domain according to the relative size of the scaling parameter ε \varepsilon : in the regime where ε \varepsilon is large, we design a weighted norm to mitigate the issue caused by the fat tail, while in the regime where ε \varepsilon is small, we prove a strong convergence of LFP towards its fractional diffusion limit with an explicit convergence rate. This method extends the traditional AP estimates to cases where uniform bounds are unavailable. Our result applies to any dimension and to the whole span of the fractional power.

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来源期刊
Mathematics of Computation
Mathematics of Computation 数学-应用数学
CiteScore
3.90
自引率
5.00%
发文量
55
审稿时长
7.0 months
期刊介绍: All articles submitted to this journal are peer-reviewed. The AMS has a single blind peer-review process in which the reviewers know who the authors of the manuscript are, but the authors do not have access to the information on who the peer reviewers are. This journal is devoted to research articles of the highest quality in computational mathematics. Areas covered include numerical analysis, computational discrete mathematics, including number theory, algebra and combinatorics, and related fields such as stochastic numerical methods. Articles must be of significant computational interest and contain original and substantial mathematical analysis or development of computational methodology.
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