FMIFlux.jl

FMIFlux.jl is a free-to-use software library for the Julia programming language, which offers the ability to set up NeuralFMUs just like NeuralODEs: You can place FMUs (fmi-standard.org) simply inside any feed-forward ANN topology and keep the resulting hybrid model trainable with a standard (or custom) FluxML training process.
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33 Stars
Updated Last
11 Months Ago
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May 2021

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FMIFlux.jl

What is FMIFlux.jl?

FMIFlux.jl is a free-to-use software library for the Julia programming language, which offers the ability to set up NeuralFMUs just like NeuralODEs: 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 (or custom) FluxML training process.

Dev Docs Test (latest) Test (LTS) Examples Build Docs Run PkgEval Coverage ColPrac: Contributor's Guide on Collaborative Practices for Community Packages

How can I use FMIFlux.jl?

1. Open a Julia-REPL, switch to package mode using ], activate your preferred environment.

2. Install FMIFlux.jl:

(@v1.x) pkg> add FMIFlux

3. If you want to check that everything works correctly, you can run the tests bundled with FMIFlux.jl:

(@v1.x) pkg> test FMIFlux

4. Have a look inside the examples folder in the examples branch or the examples section of the documentation. All examples are available as Julia-Script (.jl), Jupyter-Notebook (.ipynb) and Markdown (.md).

What is currently supported in FMIFlux.jl?

  • building and training ME-NeuralFMUs (event-handling is supported) with the default Flux-Front-End
  • building and training CS-NeuralFMUs with the default Flux-Front-End
  • different AD-frameworks: ForwardDiff.jl, ReverseDiff.jl (default setting) and Zygote.jl
  • ...

What is under development in FMIFlux.jl?

  • performance optimizations
  • improved documentation
  • more examples
  • ...

What Platforms are supported?

FMIFlux.jl is tested (and testing) under Julia versions 1.6 (LTS) and 1.8 (latest) on Windows (latest) and Ubuntu (latest). MacOS should work, but untested. However, please use Julia versions >= 1.7 if possible, because FMIFlux.jl runs a lot faster with these newer Julia versions. FMIFlux.jl currently only works with FMI2-FMUs. All shipped examples are tested under Julia version 1.8 (latest) on Windows (latest).

What FMI.jl-Library should I use?

FMI.jl Family To keep dependencies nice and clean, the original package FMI.jl had been split into new packages:

  • FMI.jl: High level loading, manipulating, saving or building entire FMUs from scratch
  • FMIImport.jl: Importing FMUs into Julia
  • FMIExport.jl: Exporting stand-alone FMUs from Julia Code
  • FMICore.jl: C-code wrapper for the FMI-standard
  • FMIBuild.jl: Compiler/Compilation dependencies for FMIExport.jl
  • FMIFlux.jl: Machine Learning with FMUs (differentiation over FMUs)
  • FMIZoo.jl: A collection of testing and example FMUs

How to cite?

Tobias Thummerer, Johannes Stoljar and Lars Mikelsons. 2022. NeuralFMU: presenting a workflow for integrating hybrid NeuralODEs into real-world applications. Electronics 11, 19, 3202. DOI: 10.3390/electronics11193202

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

Related publications?

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