FMIFlux.jl is a free-to-use software library for the Julia programming language, which offers the ability to setup NeuralFMUs: You can place FMUs (fmi-standard.org) simply inside any feed-forward ANN topology and still keep the resulting hybrid model trainable with a standard AD training process.

1. open a Julia-Command-Window, activate your prefered environemnt
2. goto package manager using ]
3. type add FMIFlux or add "https://github.com/ThummeTo/FMIFlux.jl"
4. have a look in the example folder

• building and training ME-NeuralFMUs (no event-handling) with the default Flux-Front-End
• building and training CS-NeuralFMUs with the default Flux-Front-End
• easy access to sensitivities over FMUs using fmiGetJacobian
• ...

• training ME-NeuralFMUs with state- and time-event-handling

• performance optimizations
• different modes for sensitivity estimation
• improved documentation
• more examples
• ...

FMIFlux.jl is tested (and testing) under Julia Version 1.6 on Windows (latest). Linux & Mac should work, but untested.

Tobias Thummerer, Lars Mikelsons and Josef Kircher. 2021. NeuralFMU: towards structural integration of FMUs into neural networks. Martin Sjölund, Lena Buffoni, Adrian Pop and Lennart Ochel (Ed.). Proceedings of 14th Modelica Conference 2021, Linköping, Sweden, September 20-24, 2021. Linköping University Electronic Press, Linköping (Linköping Electronic Conference Proceedings ; 181), 297-306. DOI: 10.3384/ecp21181297

Tobias Thummerer, Johannes Tintenherr, Lars Mikelsons 2021 Hybrid modeling of the human cardiovascular system using NeuralFMUs Journal of Physics: Conference Series 2090, 1, 012155. DOI: 10.1088/1742-6596/2090/1/012155