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We consider a shape implant design problem that arises in the context of facial surgery.
We introduce a reformulation as an optimal control problem, where the control acts
as a boundary force. The state is modelled as a minimizer of a polyconvex
hyperelastic energy functional. We show existence of optimal solutions and
derive - on a formal level - first order optimality conditions. Finally, preliminary numerical results
are presented.

Facial trauma or congenital malformation of bones of the skull may degrade both skeletal integrity as well as the esthetic appearance. For the attending surgeon a prediction of the esthetic outcome of a bone replacement or augmentation implant insertion is challenging. Therefore, it would be advantageous if we were able to compute an implant shape from a given desired outcome. This task presents the main focus of this thesis. Besides the development of a model for the implant shape design problem, this work is concerned with the efficient solution and optimization of realistic models. This includes recent material laws for different soft tissue types as well as complex geometries attained from medical image data.
The implant shape design problem can be described as an optimal control problem with constraints given by the necessary optimality conditions in polyconvex hyperelasticity with nonlinear pressure-type boundary conditions. Important theoretical results, such as existence of solutions and higher regularity, are currently not available for such problems. Based on the existence result for polyconvex materials laws, existence of solutions of the nonconvex optimal control problem is proven for the case of a simpler Neumann boundary condition.
Due to the “impossible convexity” and the high nonlinearity of hyperelastic material laws the numerical solution of the arising problems is difficult. In this regard, an affine covariant composite step method for nonconvex, equality constrained optimization is presented. The corresponding globalization strategy is based on the affine covariant Newton method for underdetermined systems and cubic regularization methods for unconstrained optimization problems.
The linear systems arising from the discretization of constrained optimization problems are described by saddle point matrices. The efficient solution of these equality systems by conjugate gradient methods for convex and nonconvex problems is discussed. Moreover, an error estimator that fits into the affine covariant setting is presented.
The presented composite step method was implemented in the C++ finite element library Kaskade 7. The performance of the algorithm is demonstrated on several examples. Next to simple optimization problems, with admissible set given through models of linear and nonlinear heat transfer, we give four examples with nonconvex, hyperelastic constraints.

We propose a composite step method, designed for equality constrained optimization with partial differential equations. Focus is laid on the construction of a globalization scheme, which is based on cubic regularization of the objective and an affine covariant damped Newton method for feasibility. We show finite termination of the inner loop and fast local convergence of the algorithm. We discuss preconditioning strategies for the iterative solution of the arising linear systems with projected conjugate gradient. Numerical results are shown for optimal control problems subject to a nonlinear heat equation and subject to nonlinear elastic equations arising from an implant design problem in craniofacial surgery.