/FMIFlux.jl

Primary LanguageJuliaMIT LicenseMIT

FMIFlux.jl Logo

FMIFlux.jl

What is FMIFlux.jl?

FMIFlux.jl is a free-to-use software library for the Julia programming language, which offers the ability to set up NeuralFMUs just like NeuralODEs: You can place FMUs (fmi-standard.org) simply inside any feed-forward ANN topology and still keep the resulting hybrid model trainable with a standard AD training process.

Dev Docs CI Testing Coverage

How can I use FMIFlux.jl?

  1. open a Julia-Command-Window, activate your preferred environment
  2. goto package manager using ]
  3. type add FMIFlux or add "https://github.com/ThummeTo/FMIFlux.jl"
  4. have a look in the example folder

What is currently supported in FMIFlux.jl?

  • building and training ME-NeuralFMUs (event-handling is BETA) with the default Flux-Front-End
  • building and training CS-NeuralFMUs with the default Flux-Front-End
  • ...

What is currently BETA-supported in FMIFlux.jl?

  • training ME-NeuralFMUs with state- and time-event-handling

What is under development in FMIFlux.jl?

  • performance optimizations
  • different modes for sensitivity estimation
  • improved documentation
  • more examples
  • ...

What Platforms are supported?

FMIFlux.jl is tested (and testing) under Julia versions 1.6.5 LTS and latest on Windows (latest). Linux & Mac should work, but untested.

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)

How to cite? 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