Open Access
October, 2023 Filter Regularization Method for Inverse Source Problem of the Rayleigh–Stokes Equation
Songshu Liu
Author Affiliations +
Taiwanese J. Math. 27(5): 847-861 (October, 2023). DOI: 10.11650/tjm/230302

Abstract

In this paper, we consider a problem of recovering a space-dependent source term for the Rayleigh–Stokes equation, where the additional data is the observation at a final moment $t = T$, which is ill-posed in the sense of Hadamard. Firstly, the uniqueness, ill-posedness and the conditional stability of inverse source problem is given. Next, we develop a filter regularization method to overcome the ill-posedness of the problem. Under reasonable a priori bound assumption about the source function, a Hölder-type error estimate of the regularized solution is proved for a priori regularization parameter choice rule. Furthermore, a logarithmic-type error estimate between the exact solution and the regularized solution is established based on a posteriori regularization parameter choice rule.

Funding Statement

This research is supported by Research Project of Higher School Science and Technology in Hebei Province (QN2021305).

Acknowledgments

The author would like to thanks the editor and the referees for their valuable comments and suggestions that improve the quality of our paper.

Citation

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Songshu Liu. "Filter Regularization Method for Inverse Source Problem of the Rayleigh–Stokes Equation." Taiwanese J. Math. 27 (5) 847 - 861, October, 2023. https://doi.org/10.11650/tjm/230302

Information

Received: 23 May 2022; Revised: 10 February 2023; Accepted: 29 March 2023; Published: October, 2023
First available in Project Euclid: 19 September 2023

MathSciNet: MR4643458
Digital Object Identifier: 10.11650/tjm/230302

Subjects:
Primary: 35R11 , 35R25 , 35R30 , 47A52

Keywords: convergence estimates , filter regularization method , Ill-posed problem , inverse source problem , Rayleigh–Stokes equation

Rights: Copyright © 2023 The Mathematical Society of the Republic of China

Vol.27 • No. 5 • October, 2023
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