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.
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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 FMIImport3. If you want to check that everything works correctly, you can run the tests bundled with FMIImport.jl:
(@v1) pkg> test FMIImport4. 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).
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
FMIImport.jl is tested (and testing) under Julia LTS and latest on Windows latest and Ubuntu latest (x64). Other platforms (macOS, x86, aarch64) might work, but are not CI-tested.
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 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
Contributors are welcome. Before contributing, please read, understand and follow the Contributor's Guide on Collaborative Practices for Community Packages. During development of new implementations or optimizations on existing code, one will have to make design decisions that influence the library performance and usability. The following prioritization should be the basis for decision-making:
- #1 Compliance with standard: It is the highest priority to be compliant with the FMI standard (fmi-standard.org). Identifiers described in the standard must be used. Structures and topologies should follow the specification as far as the possibilities of the Julia programming language allows.
- #2 Performance: Because the FMI.jl family is used for simulation, performance is very important. This applies to the efficient use of CPU and GPU, but also the conscientious use of RAM and disc space.
- #3 Usability: The library should be as usable as possible and feel "the Julia way" (e.g. by using multiple dispatch instead of the "C coding style"), as long as being fully compliant with the FMI standard.