# NeuralFMU: Towards Structural Integration of FMUs into Neural Networks

arXiv: https://arxiv.org/abs/2109.04351 · HTML: https://mlsanity.com/paper/2109.04351/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 9 Sep 2021.

## Builds on (most cited first, 4 of 4)

- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [A Differentiable Programming System to Bridge Machine Learning and Scientific Computing](https://mlsanity.com/paper/1907.07587/) (2019)
- [DiffEqFlux.jl - A Julia Library for Neural Differential Equations](https://mlsanity.com/paper/1902.02376/) (2019)
- [Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes](https://mlsanity.com/paper/2006.09914/) (2020)

## Built on by (most cited first, 3 of 3)

- [NeuralFMU: Presenting a workflow for integrating hybrid NeuralODEs into real world applications](https://mlsanity.com/paper/2209.03933/) (2022)
- [Hybrid modeling of the human cardiovascular system using NeuralFMUs](https://mlsanity.com/paper/2109.04880/) (2021)
- [Eigen-informed NeuralODEs: Dealing with stability and convergence issues of NeuralODEs](https://mlsanity.com/paper/2302.10892/) (2023)

## Abstract (arXiv)

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.