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Copy file name to clipboardexpand all lines: README.md
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## What is FMICore.jl?
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[*FMICore.jl*](https://github.com/ThummeTo/FMICore.jl) implements the low-level equivalents of the C-functions and C-data types of the FMI-standard ([fmi-standard.org](http://fmi-standard.org/)) for the Julia programming language.
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[*FMICore.jl*](https://github.com/ThummeTo/FMICore.jl) provides the foundation for the Julia packages [*FMIImport.jl*](https://github.com/ThummeTo/FMIImport.jl) and [*FMIExport.jl*](https://github.com/ThummeTo/FMIExport.jl).
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-[*FMI.jl*](https://github.com/ThummeTo/FMI.jl): High level loading, manipulating, saving or building entire FMUs from scratch
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-[*FMIImport.jl*](https://github.com/ThummeTo/FMIImport.jl): Importing FMUs into Julia
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-[*FMIExport.jl*](https://github.com/ThummeTo/FMIExport.jl): Exporting stand-alone FMUs from Julia Code
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-[*FMIBase.jl*](https://github.com/ThummeTo/FMIBase.jl): Common concepts for import and export of FMUs
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-[*FMICore.jl*](https://github.com/ThummeTo/FMICore.jl): C-code wrapper for the FMI-standard
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-[*FMISensitivity.jl*](https://github.com/ThummeTo/FMISensitivity.jl): Static and dynamic sensitivities over FMUs
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-[*FMIBuild.jl*](https://github.com/ThummeTo/FMIBuild.jl): Compiler/Compilation dependencies for FMIExport.jl
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-[*FMIFlux.jl*](https://github.com/ThummeTo/FMIFlux.jl): Machine Learning with FMUs (differentiation over FMUs)
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-[*FMIFlux.jl*](https://github.com/ThummeTo/FMIFlux.jl): Machine Learning with FMUs
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-[*FMIZoo.jl*](https://github.com/ThummeTo/FMIZoo.jl): A collection of testing and example FMUs
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## What Platforms are supported?
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[*FMICore.jl*](https://github.com/ThummeTo/FMICore.jl) is tested (and testing) under Julia Versions *v1.6 LTS* and *v1 latest* on Windows *latest*, Ubuntu *latest*and MacOS*latest*. `x64` and `x86` architectures are tested.
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[*FMICore.jl*](https://github.com/ThummeTo/FMICore.jl) is tested (and testing) under Julia Versions *v1.6 LTS* and *v1 latest* on Windows *latest* (`x64` and `x86`) and Ubuntu*latest* (`x64`). MacOS is not CI-tested but should work with Mac-FMUs.
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## How to cite?
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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](https://doi.org/10.3384/ecp21181297)
Source: FMISpec2.0.2[p.18]: 2.1.3 Status Returned by Functions
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- fmi2Fatal – the model computations are irreparably corrupted for all FMU instances. [For example, due to a run-time exception such as access violation or integer division by zero during the execution of an fmi function]. Function “logger” was called in the FMU (see below), and it is expected that this function has shown the prepared information message to the user. It is not possible to call any other function for any of the FMU instances.
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- fmi2Pending – this status is returned only from the co-simulation interface, if the slave executes the function in an asynchronous way. That means the slave starts to compute but returns immediately. The master has to call fmi2GetStatus(..., fmi2DoStepStatus) to determine if the slave has finished the computation. Can be returned only by fmi2DoStep and by fmi2GetStatus (see section 4.2.3).
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