A mollifier approach to the simultaneous identification of the unknown source and initial distribution in a space-fractional diffusion equation

Yu Qiao and Xiangtuan Xiong

Applied Mathematics and Computation, 2025, vol. 489, issue C

Abstract: In this paper, a simultaneous inversion problem for the Riesz-Feller space-fractional diffusion equation with inexact operators is investigated, which is to identify the source term and initial value from two over-specified measurements. The problem model is well known to be ill-posed. We propose a regularization method to deal with the inverse problem using the idea of mollification. Under an a priori and an a posteriori parameter choice rules, we derive explicit error estimates between the exact solutions and their regularized approximations in the practical case where both the operators and the data are noisy. Numerical results show that the proposed method is efficient, and the unknown terms are recovered quite well.

Keywords: Ill-posed problem; Mollification; Regularization; Error estimates (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0096300324006362
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:489:y:2025:i:c:s0096300324006362

DOI: 10.1016/j.amc.2024.129175

Access Statistics for this article

Applied Mathematics and Computation is currently edited by Theodore Simos

More articles in Applied Mathematics and Computation from Elsevier
Bibliographic data for series maintained by Catherine Liu ().