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FMIImport.jl

What is FMIImport.jl?

FMIImport.jl implements the import functionalities of the FMI-standard (fmi-standard.org) for the Julia programming language. FMIImport.jl provides the foundation for the Julia packages FMI.jl and FMIFlux.jl.

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

How can I use FMIImport.jl?

FMIImport.jl is part of FMI.jl. However, if you only need the import functionality without anything around and want to keep the dependencies as small as possible, FMIImport.jl might be the right way to go. You can install it via:

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

2. Install FMIImport.jl:

(@v1) pkg> add FMIImport

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

(@v1) pkg> test FMIImport

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

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
  • FMIBase.jl: Common concepts for import and export of FMUs
  • 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
  • FMIZoo.jl: A collection of testing and example FMUs

What Platforms are supported?

FMIImport.jl is tested (and testing) under Julia Versions 1.6 LTS and latest on Windows latest and Ubuntu latest. x64 architectures are tested. Mac and x86-architectures might work, but are not tested.

How to cite?

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 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, 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