  Testing new, innovative technologies is a crucial task for safety and
acceptance. But how can new systems be tested if no historical real-world data
exist? Simulation provides an answer to this important question. Classical
simulation tools such as event-based simulation are well accepted. But most of
these established simulation models require the specification of many
parameters. Furthermore, simulation runs, e.g., CFD simulations, are very time
consuming. Generative Adversarial Networks (GANs) are powerful tools for
generating new data for a variety of tasks. Currently, their most frequent
application domain is image generation. This article investigates the
applicability of GANs for imitating simulations. We are comparing the
simulation output of a technical system with the output of a GAN. To exemplify
this approach, a well-known multi-car elevator system simulator was chosen. Our
study demonstrates the feasibility of this approach. It also discusses pitfalls
and technical problems that occurred during the implementation. Although we
were able to show that in principle, GANs can be used as substitutes for
expensive simulation runs, we also show that they cannot be used "out of the
box". Fine tuning is needed. We present a proof-of-concept, which can serve as
a starting point for further research.

Machine Learning;Machine Learning