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
“…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:…”
In this work we address the inverse problem of reconstructing inclusions and their thermal parameters given temperature measurements at the accessible side of a material. We describe an iterative descent method that combines topological derivative computations to reconstruct the geometry of the defects with gradient iterations to approximate the material parameters. A numerical experiment showing the ability of the method to obtain reasonable reconstructions in a few iterations is presented.
“…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:…”
In this work we address the inverse problem of reconstructing inclusions and their thermal parameters given temperature measurements at the accessible side of a material. We describe an iterative descent method that combines topological derivative computations to reconstruct the geometry of the defects with gradient iterations to approximate the material parameters. A numerical experiment showing the ability of the method to obtain reasonable reconstructions in a few iterations is presented.
“…[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].…”
We investigate the numerical reconstruction of smooth star-shaped voids (rigid inclusions and cavities) which are compactly contained in a three-dimensional isotropic linear elastic medium from a single set of Cauchy data (i.e. nondestructive boundary displacement and traction measurements) on the accessible outer boundary. This inverse geometric problem in three-dimensional elasticity is approximated using the method of fundamental solutions (MFS). The parameters describing the boundary of the unknown void, its centre, and the contraction and dilation factors employed for selecting the fictitious surfaces where the MFS sources are to be positioned, are taken as unknowns of the problem. In this way, the original inverse geometric problem is reduced to finding the minimum of a nonlinear least-squares functional that measures the difference between the given and computed data, penalized with respect to both the MFS constants and the derivative of the radial coordinates describing the position of the star-shaped void. The interior source points are anchored and move with the void during the iterative reconstruction procedure. The feasibility of this new method is illustrated in several numerical examples.
“…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.…”
Unsteady fluid-structure interaction (FSI) simulations are generally time-consuming. Gradient-based methods are preferred to minimise the computational cost of parameter identification studies (and more in general optimisation) with a high number of parameters. However, calculating the cost function's gradient using finite differences becomes prohibitively expensive for a high number of parameters. Therefore, the adjoint equations of the unsteady FSI problem are solved to obtain this gradient at a cost almost independent of the number of parameters.Here, both the forward and the adjoint problems are solved in a partitioned way, which means that the flow equations and the structural equations are solved separately. The application of interest is the identification of the arterial wall's stiffness by comparing the motion of the arterial wall with a reference, possibly obtained from non-invasive imaging. Due to the strong interaction between the fluid and the structure, quasi-Newton coupling iterations are applied to stabilise the partitioned solution of both the forward and the adjoint problem.
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