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Unfortunately, FMUs (fmi-standard.org) are not differentiable by design. To enable their full potential inside Julia, FMISensitivity.jl makes FMUs fully differentiable, regarding to: states and derivatives | inputs, outputs and other observable variables | parameters | event indicators | explicit time | state change sensitivity by event

License: MIT License

Julia 100.00%
fmi fmu linearization sciml sensitivity uncertainty

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fmisensitivity.jl's Issues

AD Framework specific dispatch

Different AD frameworks have different requirements.
The common ChainRules interface, that is currently used, is not optimal in terms of allocations (there are allocations necessary because of type restrictions to be compliant with all AD frameworks, even if only one specific framework actually needs them).

  • Implement a dedicated ReverseDiff-Dispatch for eval/rrule
  • inplace eval! function for FMU2Output
  • Improve the common (ChainRules) functions eval/frule/rrule, so they don't need to cover for the ReverseDiff case (like e.g. this issue)

TagBot trigger issue

This issue is used to trigger TagBot; feel free to unsubscribe.

If you haven't already, you should update your TagBot.yml to include issue comment triggers.
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If you'd like for me to do this for you, comment TagBot fix on this issue.
I'll open a PR within a few hours, please be patient!

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