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Third-order time stepping methods for superdiffusion using weighted and shifted Grünwald–Letnikov formulae with nonsmooth data
He, Yonghui ; Chen, Jinghua ; Yan, Yubin ; Chen, Xuejuan ; Liu, Xinran ; Ding, Peng
He, Yonghui
Chen, Jinghua
Yan, Yubin
Chen, Xuejuan
Liu, Xinran
Ding, Peng
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Jimei University; University of Chester; Xiamen University
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2025-12-02
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- Embargoed until 2026-12-02
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Abstract
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/s10915-025-03088-5
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Springer
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Journal of Scientific Computing
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Article
Language
en
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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/s10915-025-03088-5
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0885-7474
EISSN
1573-7691
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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.
