Two regularization methods for identifying the source term of Caputo–Hadamard type time fractional diffusion-wave equation

Abstract In this paper, the inverse problem of source term identification for Caputo–Hadamard type time-fractional diffusion-wave equation is studied. Firstly, we prove that the problem is ill-posed, and give the optimal error bound and the conditional stability results. Secondly, we apply fractional Tikhonov regularization method and fractional Landweber iterative regularization method to solve the problem. Based on the conditional stability results, we give error estimates under the a priori regularization parameter selection rule and the a posteriori regularization parameter selection rule respectively. In addition, we give three numerical examples to prove the validity and feasibility of the selected regularization method. What is novel is that we apply L2 1 formula, Crank–Nicolson format and the finite difference method to discrete equation.

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Publication Details

Journal
Journal of Inverse and Ill-Posed Problems
Published
2025-04-09
DOI
https://doi.org/10.1515/jiip-2024-0051
Citations
9
Primary Topic
Fractional Differential Equations Solutions
Type
article
Field-Weighted Citation Impact
12.31

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article

Two regularization methods for identifying the source term of Caputo–Hadamard type time fractional diffusion-wave equation

Ruo-Hong Li, Fan Yang, Yin-Xia Gao, Xiaoxiao Li
9 citations
Journal of Inverse and Ill-Posed Problems
Fractional Differential Equations Solutions
12.31
article

Two regularization methods for identifying the source term of Caputo–Hadamard type time fractional diffusion-wave equation

Ruo-Hong Li, Fan Yang, Yin-Xia Gao, Xiaoxiao Li
article en
9 citations

Abstract

Abstract In this paper, the inverse problem of source term identification for Caputo–Hadamard type time-fractional diffusion-wave equation is studied. Firstly, we prove that the problem is ill-posed, and give the optimal error bound and the conditional stability results. Secondly, we apply fractional Tikhonov regularization method and fractional Landweber iterative regularization method to solve the problem. Based on the conditional stability results, we give error estimates under the a priori regularization parameter selection rule and the a posteriori regularization parameter selection rule respectively. In addition, we give three numerical examples to prove the validity and feasibility of the selected regularization method. What is novel is that we apply L2 1 formula, Crank–Nicolson format and the finite difference method to discrete equation.

Journal of Inverse and Ill-Posed Problems
Lanzhou University of Technology (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 1%
Fractional Differential Equations Solutions
12.31
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