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What is FMIExport.jl?

FMIExport.jl is a free-to-use software library for the Julia programming language which allows for the export of FMUs ( from any Julia-Code. FMIExport.jl is completely integrated into FMI.jl.

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How can I use FMIExport.jl?

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

2. Install FMIExport.jl:

(@v1) pkg> add FMIExport

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

(@v1) pkg> test FMIExport

4. Have a look inside the examples folder.

What FMI.jl-Library should I use?

FMI.jl Logo 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
  • FMISensitivity.jl: Static and dynamic sensitivities over FMUs
  • 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

What Platforms are supported?

FMIExport.jl is tested (and testing) under Julia v1.6 (LTS) and v1 (latest) on Windows latest. x64 architectures are tested. Ubuntu is under development and near completion.

Known limitations

  • Currently it is not possible to hold more than one sysimg in a Julia session, so it is not possible to load FMUs generated with FMIExport.jl into FMI.jl using fmiLoad(...). However, FMUs created with fmiCreate can be used in FMI.jl without the need to export and re-import it, the FMU behaves like any other FMU loaded via fmiLoad.

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

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