Numerical methods for forward fractional Feynman–Kac equation

IF 1.7 3区 数学 Q2 MATHEMATICS, APPLIED Advances in Computational Mathematics Pub Date : 2024-06-10 DOI:10.1007/s10444-024-10152-5
Daxin Nie, Jing Sun, Weihua Deng
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引用次数: 0

Abstract

Fractional Feynman–Kac equation governs the functional distribution of the trajectories of anomalous diffusion. The non-commutativity of the integral fractional Laplacian and time-space coupled fractional substantial derivative, i.e., \(\mathcal {A}^{s}{}_{0}\partial _{t}^{1-\alpha ,x}\ne {}_{0}\partial _{t}^{1-\alpha ,x}\mathcal {A}^{s}\), brings about huge challenges on the regularity and spatial error estimates for the forward fractional Feynman–Kac equation. In this paper, we first use the corresponding resolvent estimate obtained by the bootstrapping arguments and the generalized Hölder-type inequalities in Sobolev space to build the regularity of the solution, and then the fully discrete scheme constructed by convolution quadrature and finite element methods is developed. Also, the complete error analyses in time and space directions are respectively presented, which are consistent with the provided numerical experiments.

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前向分数费曼-卡克方程的数值方法
分数费曼-卡克方程控制着反常扩散轨迹的函数分布。积分分数拉普拉斯和时空耦合分数实质导数的非交换性,即\(\mathcal {A}^{s}{}_{0}\partial _{t}^{1-\alpha ,x}\ne {}_{0}\partial _{t}^{1-\alpha ,x}\mathcal {A}^{s}\),给前向分数费曼-卡克方程的正则性和空间误差估计带来了巨大挑战。在本文中,我们首先利用引导论证得到的相应分解估计和 Sobolev 空间中的广义 Hölder 型不等式建立解的正则性,然后利用卷积正交和有限元方法建立全离散方案。此外,还分别给出了时间和空间方向的完整误差分析,与所提供的数值实验结果相一致。
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来源期刊
CiteScore
3.00
自引率
5.90%
发文量
68
审稿时长
3 months
期刊介绍: Advances in Computational Mathematics publishes high quality, accessible and original articles at the forefront of computational and applied mathematics, with a clear potential for impact across the sciences. The journal emphasizes three core areas: approximation theory and computational geometry; numerical analysis, modelling and simulation; imaging, signal processing and data analysis. This journal welcomes papers that are accessible to a broad audience in the mathematical sciences and that show either an advance in computational methodology or a novel scientific application area, or both. Methods papers should rely on rigorous analysis and/or convincing numerical studies.
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