Third-order time stepping methods for superdiffusion using weighted and shifted Grünwald–Letnikov formulae with nonsmooth data
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Jimei University; University of Chester; Xiamen UniversityPublication Date
2025-12-02
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In this paper, we study a numerical method for the Caputo time fractional wave equation with nonsmooth data. We first introduce a class of third-order approximations, known as weighted and shifted Grünwald-Letnikov approximations, to approximate the Caputo fractional derivative. Based on this, we develop a new time stepping method for solving the time fractional wave equation. After applying corrections to several initial steps, the proposed time stepping method achieves a convergence order of O(k3)$$O(k^3)$$ for nonsmooth data, where k denotes the time step size. We also analyze the stability regions of the proposed time stepping method, which show that the scheme is unconditionally stable for α∈(1,1.94)$$ \alpha \in (1, 1.94) $$, and conditionally stable for α∈[1.94,2)$$ \alpha \in [1.94, 2) $$. Numerical experiments are presented to validate the theoretical findings.Citation
He, Y., Chen, J., Yan, Y., Chen, X., Liu, X., & Ding, P. (2026). Third-order time stepping methods for superdiffusion using weighted and shifted Grünwald–Letnikov formulae with nonsmooth data. Journal of Scientific Computing, 106, article number 4. https://doi.org/10.1007/s10915-025-03088-5Publisher
SpringerJournal
Journal of Scientific ComputingAdditional Links
https://link.springer.com/article/10.1007/s10915-025-03088-5Type
ArticleLanguage
enDescription
This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s10915-025-03088-5ISSN
0885-7474EISSN
1573-7691Sponsors
The work of this paper was partially funded by the Fujian Province Natural Science Foundation 2022J01338, Fujian Province Education Fund JAT210231, Fujian Alliance of Mathematics 2024SXLMMS03, and Digital Fujian Big Data Modeling and Intelligent Computing Institute, China.ae974a485f413a2113503eed53cd6c53
10.1007/s10915-025-03088-5
