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This paper covers two major subjects: First, the presentation of a newopen-source library called FMI.jl for integrating FMI into the Juliaprogramming environment by providing the possibility to load, parameterize andsimulate FMUs. Further, an extension to this library called FMIFlux.jl isintroduced, that allows the integration of FMUs into a neural network topologyto obtain a NeuralFMU. This structural combination of an industry typicalblack-box model and a data-driven machine learning model combines the differentadvantages of both modeling approaches in one single development environment.This allows for the usage of advanced data driven modeling techniques forphysical effects that are difficult to model based on first principles.

NeuralFMU: Towards Structural Integration of FMUs into Neural Networks