  Dielectric axion haloscopes, such as the MADMAX experiment, are promising
concepts for the direct search for dark matter axions. A reliable simulation is
a fundamental requirement for the successful realisation of the experiments.
Due to the complexity of the simulations, the demands on computing resources
can quickly become prohibitive. In this paper, we show for the first time that
modern deep learning techniques can be applied to aid the simulation and
optimisation of dielectric haloscopes.
