4.7 Article

Simultaneous inversion of a time-dependent potential coefficient and a time source term in a time fractional diffusion-wave equation

Journal

CHAOS SOLITONS & FRACTALS
Volume 157, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.chaos.2022.111901

Keywords

Nonlinear inverse problem; Stability and uniqueness; Ill-posedness; Non-stationary iterative Tikhonov regularization method

Funding

  1. NSF of China [12171215, 11771192]
  2. NSF of Gansu Province [21JR7RA475]

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The main purpose of this paper is to simultaneously identify a time-dependent potential coefficient and a time source term in a time fractional diffusion-wave equation. The existence and uniqueness of the solution for the direct problem are proved using the fixed point theorem, and the uniqueness of the inverse problem is a direct result of the stability estimate. Additionally, a non-stationary iterative Tikhonov regularization method is used to recover the time dependent potential coefficient and source term, and an alternating minimization method is applied to solve the minimization problem.
The main purpose of this paper is to identify simultaneously a time-dependent potential coefficient and a time source term in a time fractional diffusion-wave equation from two points observed data. First of all, using the fixed point theorem, we prove the existence and uniqueness of the solution for the direct problem. Secondly, the stability of the inverse problem is proved and the uniqueness is a direct result of the stability estimate. In addition, we illustrate the ill-posedness of the inverse problem and use a non-stationary iterative Tikhonov regularization method to recover numerically the time dependent potential coefficient and source term. At the same time, we give the existence of the minimizer for the minimization functional. In order to solve the minimization problem, we apply an alternating minimization method to find the minimizer and prove the solving sub-problems are stable on noisy data as well as prove the data fidelity item decreases monotonously with the iterative running. Finally, some numerical examples are provided to shed light on the validity and robustness of the numerical algorithm. (C) 2022 Elsevier Ltd. All rights reserved.

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