2005
DOI: 10.1088/0266-5611/21/2/r01
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Inverse problems in elasticity

Abstract: Abstract. This article is devoted to some inverse problems arising in the context of linear elasticity, namely the identification of distributions of elastic moduli, model parameters, or buried objects such as cracks. These inverse problems are considered mainly for threedimensional elastic media under equilibrium or dynamical conditions, and also for thin elastic plates. The main goal is to overview some recent results, in an effort to bridge the gap between studies of a mathematical nature and problems defin… Show more

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Cited by 360 publications
(323 citation statements)
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“…This has been tested in a wide range of physical settings, including acoustics, electromagnetism, elastodynamics, electrical impedance tomography, fluorescence optical tomography, and photothermal imaging [1,5,7,8,10,15,20].…”
Section: Iterative Methods To Reconstruct Inclusions and Parametersmentioning
confidence: 99%
See 1 more Smart Citation
“…This has been tested in a wide range of physical settings, including acoustics, electromagnetism, elastodynamics, electrical impedance tomography, fluorescence optical tomography, and photothermal imaging [1,5,7,8,10,15,20].…”
Section: Iterative Methods To Reconstruct Inclusions and Parametersmentioning
confidence: 99%
“…In the exterior domain Ω e , the total temperature U total = U + + U inc is the superposition of U + and the incident wave defined in (1). The temperature satisfies the following transmission conditions at the common interface:…”
Section: Statement Of the Problemmentioning
confidence: 99%
“…[16]. Such inverse problems have been extensively studied, both theoretically and numerically, over the last three decades and an overview of these developments can be found in [10].…”
Section: Introductionmentioning
confidence: 99%
“…Such a simulation solves a so-called forward (or direct) problem. The corresponding inverse problem is to find parameter values such that the data resulting from a forward simulation with these parameter values agrees as closely as possible with some measurement (or target) [3]. Parameter identification techniques to solve these inverse problems can generally be reformulated as a minimisation problem.…”
Section: Introductionmentioning
confidence: 99%