@phdthesis{Merger2016,
  author    = {Merger, Juri},
  title     = {Optimal Control and Function Identification in Biological Processes},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-138900},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2016},
  abstract  = {Mathematical modelling, simulation, and optimisation are core methodologies for future developments in engineering, natural, and life sciences. This work aims at applying these mathematical techniques in the field of biological processes with a focus on the wine fermentation process that is chosen as a representative model. In the literature, basic models for the wine fermentation process consist of a system of ordinary differential equations. They model the evolution of the yeast population number as well as the concentrations of assimilable nitrogen, sugar, and ethanol. In this thesis, the concentration of molecular oxygen is also included in order to model the change of the metabolism of the yeast from an aerobic to an anaerobic one. Further, a more sophisticated toxicity function is used. It provides simulation results that match experimental measurements better than a linear toxicity model. Moreover, a further equation for the temperature plays a crucial role in this work as it opens a way to influence the fermentation process in a desired way by changing the temperature of the system via a cooling mechanism. From the view of the wine industry, it is necessary to cope with large scale fermentation vessels, where spatial inhomogeneities of concentrations and temperature are likely to arise. Therefore, a system of reaction-diffusion equations is formulated in this work, which acts as an approximation for a model including computationally very expensive fluid dynamics. In addition to the modelling issues, an optimal control problem for the proposed reaction-diffusion fermentation model with temperature boundary control is presented and analysed. Variational methods are used to prove the existence of unique weak solutions to this non-linear problem. In this framework, it is possible to exploit the Hilbert space structure of state and control spaces to prove the existence of optimal controls. Additionally, first-order necessary optimality conditions are presented. They characterise controls that minimise an objective functional with the purpose to minimise the final sugar concentration. A numerical experiment shows that the final concentration of sugar can be reduced by a suitably chosen temperature control. The second part of this thesis deals with the identification of an unknown function that participates in a dynamical model. For models with ordinary differential equations, where parts of the dynamic cannot be deduced due to the complexity of the underlying phenomena, a minimisation problem is formulated. By minimising the deviations of simulation results and measurements the best possible function from a trial function space is found. The analysis of this function identification problem covers the proof of the differentiability of the function-to-state operator, the existence of minimisers, and the sensitivity analysis by means of the data-to-function mapping. Moreover, the presented function identification method is extended to stochastic differential equations. Here, the objective functional consists of the difference of measured values and the statistical expected value of the stochastic process solving the stochastic differential equation. Using a Fokker-Planck equation that governs the probability density function of the process, the probabilistic problem of simulating a stochastic process is cast to a deterministic partial differential equation. Proofs of unique solvability of the forward equation, the existence of minimisers, and first-order necessary optimality conditions are presented. The application of the function identification framework to the wine fermentation model aims at finding the shape of the toxicity function and is carried out for the deterministic as well as the stochastic case.},
  subject      = {Optimale Kontrolle},
  language  = {en}
}
@article{SchindeleBorzi2016,
  author    = {Schindele, Andreas and Borz{\`i}, Alfio},
  title     = {Proximal Methods for Elliptic Optimal Control Problems with Sparsity Cost Functional},
  series = {Applied Mathematics},
  volume    = {7},
  journal   = {Applied Mathematics},
  number    = {9},
  doi       = {10.4236/am.2016.79086},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-145850},
  pages     = {967-992},
  year      = {2016},
  abstract  = {First-order proximal methods that solve linear and bilinear elliptic optimal control problems with a sparsity cost functional are discussed. In particular, fast convergence of these methods is proved. For benchmarking purposes, inexact proximal schemes are compared to an inexact semismooth Newton method. Results of numerical experiments are presented to demonstrate the computational effectiveness of proximal schemes applied to infinite-dimensional elliptic optimal control problems and to validate the theoretical estimates.},
  language  = {en}
}