Stability analysis of a new curl-based full field reconstruction method in 2D isotropic nearly-incompressible elasticity

Abstract In time-harmonic elastography, the shear modulus is typically inferred from full-field displacement data by solving an inverse problem based on the time-harmonic elastodynamic equation. In this paper, we focus on nearly incompressible media, which pose robustness challenges, especially in the presence of noisy data. Restricting ourselves to 2D and considering an isotropic, linearly deforming medium, we reformulate the problem as a non-autonomous hyperbolic system and, through theoretical analysis, establish existence, uniqueness, and stability of the inverse problem. To ensure robustness with noisy data, we propose a least-squares approach with regularization. The convergence properties of the method are verified numerically using in silico data.

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Publication Details

Journal
Inverse Problems
Published
2026-05-13
DOI
https://doi.org/10.1088/1361-6420/ae6d2e
Primary Topic
Numerical methods in inverse problems
Type
article
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article

Stability analysis of a new curl-based full field reconstruction method in 2D isotropic nearly-incompressible elasticity

Sébastien Imperiale, Martin Genet, Nagham Chibli
Inverse Problems
Numerical methods in inverse problems
article

Stability analysis of a new curl-based full field reconstruction method in 2D isotropic nearly-incompressible elasticity

Sébastien Imperiale, Martin Genet, Nagham Chibli
article en

Abstract

Abstract In time-harmonic elastography, the shear modulus is typically inferred from full-field displacement data by solving an inverse problem based on the time-harmonic elastodynamic equation. In this paper, we focus on nearly incompressible media, which pose robustness challenges, especially in the presence of noisy data. Restricting ourselves to 2D and considering an isotropic, linearly deforming medium, we reformulate the problem as a non-autonomous hyperbolic system and, through theoretical analysis, establish existence, uniqueness, and stability of the inverse problem. To ensure robustness with noisy data, we propose a least-squares approach with regularization. The convergence properties of the method are verified numerically using in silico data.

Inverse Problems
Institut national de recherche en sciences et technologies du numérique (FR), École Polytechnique (FR), Laboratoire Mécanique des Solides (FR), Laboratoire d'Informatique de l'École Polytechnique (FR)
Openalex Percentile: Top 95%
Numerical methods in inverse problems
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