  This paper covers two major subjects: First, the presentation of a new
open-source library called FMI.jl for integrating FMI into the Julia
programming environment by providing the possibility to load, parameterize and
simulate FMUs. Further, an extension to this library called FMIFlux.jl is
introduced, that allows the integration of FMUs into a neural network topology
to obtain a NeuralFMU. This structural combination of an industry typical
black-box model and a data-driven machine learning model combines the different
advantages of both modeling approaches in one single development environment.
This allows for the usage of advanced data driven modeling techniques for
physical effects that are difficult to model based on first principles.
