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Description:
In this thesis, we consider a novel unbalanced optimal transport model incorporating singular sources, we develop a numerical computation scheme for an optimal transport distance on graphs, we propose a simultaneous elastic shape optimization problem for bone tissue engineering, and we investigate optimal material distributions on thin elastic objects. The by now classical theory of optimal transport admits a metric between measures of the same total mass. Various generalizations of this so-called Wasserstein distance have been recently studied in the literature. In particular, these have been motivated by imaging applications, where the mass-preserving condition is too restrictive. Based on the Benamou Brenier formulation we present a novel unbalanced optimal transport model by introducing a source term in the continuity equation, which is incorporated in the path energy by a squared L 2 -norm in time of a functional with linear growth in space. As a key advantage of our model, this source term functional allows singular sources in space. We demonstrate the existence of constant speed geodesics in the space of Radon measures. Furthermore, for a numerical computation scheme, we apply a proximal splitting algorithm for a finite element discretization. On discrete spaces, Maas introduced a Benamou Brenier formulation, where a kinetic energy is defined via an appropriate (e.g., logarithmic) averaging of mass on nodes and momentum on edges. Concerning a numerical optimization scheme, this, unfortunately, couples all these variables on the graph. We propose a conforming finite element discretization in time and prove convergence of corresponding path energy minimizing curves. To apply a proximal splitting algorithm, we introduce suitable auxiliary variables. Besides similar projections as for the classical optimal transport distance and additional simple operations, this allows us to separate the nonlinearity given by the averaging operator to projections onto three-dimensional convex sets, the associated (e.g., ...