Numerical Estimation of a Diffusion Coefficient in Subdiffusion

Bangti Jin, Zhi Zhou

Research output: Journal article publicationJournal articleAcademic researchpeer-review


In this work, we consider the numerical recovery of a spatially dependent diffusion coefficient in a subdiffusion model from distributed observations. The subdiffusion model involves a Caputo fractional derivative of order α in (0, 1) in time. The numerical estimation is based on the regularized output least-squares formulation, with an H1(Ω) penalty. We prove the well-posedness of the continuous formulation, e.g., existence and stability. Next, we develop a fully discrete scheme based on the Galerkin finite element method in space and backward Euler convolution quadrature in time. We prove the subsequential convergence of the sequence of discrete solutions to a solution of the continuous problem as the discretization parameters (mesh size and time step size) tend to zero. Further, under an additional regularity condition on the exact coefficient, we derive convergence rates in a weighted L2(Ω) norm for the discrete approximations to the exact coefficient in the one- and two-dimensional cases. The analysis relies heavily on suitable nonstandard nonsmooth data error estimates for the direct problem. We provide illustrative numerical results to support the theoretical study.

Original languageEnglish
Pages (from-to)1466–1496
Number of pages31
JournalSIAM Journal on Control and Optimization
Issue number2
Early online date15 Apr 2021
Publication statusPublished - Apr 2021


  • Convergence
  • Error estimate
  • Fully discrete scheme
  • Parameter identification
  • Subdiffusion
  • Tikhonov regularization

ASJC Scopus subject areas

  • Control and Optimization
  • Applied Mathematics

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