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FMIBuild.jl

What is FMIBuild.jl?

FMIBuild.jl holds dependencies that are required to compile and zip a Functional Mock-Up Unit (FMU) compliant to the FMI-standard (fmi-standard.org). Because this dependencies should not be part of the compiled FMU, they are out-sourced into this package. FMIBuild.jl provides the build-commands for the Julia package FMIExport.jl.

Run Tests Coverage

How can I use FMIBuild.jl?

Please note: FMIBuild.jl is not meant to be used as it is, but as part of FMI.jl and FMIExport.jl. However you can install FMIBuild.jl by following these steps.

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

2. Install FMIBuild.jl:

(@v1.6) pkg> add FMIBuild

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

(@v1.6) pkg> test FMIBuild

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

What Platforms are supported?

FMIBuild.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, 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

Related publications?

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