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Research Article | Open Access

Recovery of a time-dependent reaction coefficient in a space-time fractional diffusion model via Nyström discretization

Department of Mathematics, College of Science, Northern Border University, Arar, Saudi Arabia
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Abstract

We study an inverse problem for a space-time fractional diffusion equation posed on a bounded one-dimensional domain under an exterior Dirichlet condition. The model incorporates a fractional derivative in time of Caputo type and a nonlocal spatial diffusion operator given by the integral fractional Laplacian. The objective is to recover an unknown time-dependent reaction coefficient from a limited number of pointwise observations combined with multiple experiments with distinct initial conditions. We establish a rigorous functional framework for the direct problem and prove existence, uniqueness, and stability of solutions. A conditional identifiability result for the reaction coefficient is derived under a natural non-degeneracy assumption associated with the multi-experiment measurement setting. The inverse problem is formulated as a nonlinear least-squares optimization problem in a finite-dimensional parameter space based on piecewise linear basis functions. For the numerical implementation, the spatial nonlocal operator is discretized using a Nyström quadrature method, while the time-fractional derivative is approximated by a classical L 1 finite difference scheme. The resulting optimization problem is solved by a Levenberg-Marquardt (LM) algorithm, in which the sensitivity information is computed using finite-difference approximations. Several numerical experiments are presented to illustrate the effectiveness and stability of the proposed approach.

CLC number: 35R11, 35R30, 47A52, 49N45, 65K10

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AIMS Mathematics
Pages 13196-13215

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Cite this article:
Alruwaili E. Recovery of a time-dependent reaction coefficient in a space-time fractional diffusion model via Nyström discretization. AIMS Mathematics, 2026, 11(5): 13196-13215. https://doi.org/10.3934/math.2026544

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Received: 07 February 2026
Revised: 11 April 2026
Accepted: 17 April 2026
Published: 15 May 2026
©2026 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)