Abstract
In this paper, we discuss parameter identification for models based on ordinary differential equations in the context of solid oxide fuel cells. In this case, verified methods (e.g. interval analysis), which provide a guarantee of correctness for the computed result, can be of great help for dealing with the appearing uncertainty and for devising accurate control strategies. Moreover, interval arithmetic can be used to discard infeasible areas of parameter space in a natural way and so to improve the results of traditional numerical algorithms. We describe a simulation environment interfacing different verified and floating point based approaches and show how the interchangeability between different techniques enhances parameter identification. Additionally, we give details on a possible parallelization of our version of the global interval optimization algorithm on the CPU and the GPU. The applicability of the method and the features of the environment are demonstrated with the help of different fuel cell models.
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