NeuralFMU: Towards Structural Integration of FMUs into Neural Networks
Machine Learning
2022-09-09 v1
Abstract
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.
Keywords
Cite
@article{arxiv.2109.04351,
title = {NeuralFMU: Towards Structural Integration of FMUs into Neural Networks},
author = {Tobias Thummerer and Josef Kircher and Lars Mikelsons},
journal= {arXiv preprint arXiv:2109.04351},
year = {2022}
}