First-order and second-order adjoint methods for parameter identification problems with an application to the elasticity imaging inverse problem

DOI10.1080/17415977.2017.1289195zbMath1387.92057OpenAlexW2586857380MaRDI QIDQ4638156

Miguel Sama, Baasansuren Jadamba, Assad A. Oberai, Akhtar A. Khan

Publication date: 3 May 2018

Published in: Inverse Problems in Science and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/17415977.2017.1289195

zbMATH Keywords

regularizationinverse problemsill-posed problemsoutput least-squaresfirst-order adjoint methodsecond-order adjoint methodelasticity imaging inverse problem

Mathematics Subject Classification ID

Related Items (11)

On convex modified output least-squares for elliptic inverse problems: stability, regularization, applications, and numerics ⋮ Nonlinear conjugate gradient method for identifying Young's modulus of the elasticity imaging inverse problem ⋮ Levenberg-Marquardt method for identifying Young's modulus of the elasticity imaging inverse problem ⋮ An iteratively regularized stochastic gradient method for estimating a random parameter in a stochastic PDE. A variational inequality approach ⋮ Existence results and optimization over the set of efficient solutions in vector-valued approximation theory ⋮ A convex inversion framework for identifying parameters in saddle point problems with applications to inverse incompressible elasticity ⋮ Three optimization formulations for an inverse problem in saddle point problems with applications to elasticity imaging of locating tumor in incompressible medium ⋮ Analyzing the role of the Inf-Sup condition for parameter identification in saddle point problems with application in elasticity imaging ⋮ A variational inequality based stochastic approximation for inverse problems in stochastic partial differential equations ⋮ Regula falsi based automatic regularization method for PDE constrained optimization ⋮ Inverse problems for multi-valued quasi variational inequalities and noncoercive variational inequalities with noisy data

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