V0.11.0 1 (#107)

* reactive tests

* adjustments for FMISensitivity.jl

* adjustments for FMISensitivity.jl

* adjustments for FMISensitivity.jl

* modifications for FMICore 0.19.0

* updated project

* rm DiffEqCallbacks

* rm DiffEqCallbacks

* rm FMI dependency in tests

* updated tests

* optimized tests

* optimized tests

* corrected fmu_params example

* fixed test

* minor fix

* changed tests order

* fixed examples

* started example refactoring

* updated examples

* modified examples

* updated examples

* fixed examples issues

Author: ThummeTo

.github/workflows/Example.yml

@@ -19,8 +19,8 @@ jobs:
       fail-fast: false
       matrix:
         os: [windows-latest] # , ubuntu-latest]
-        file-name: [advanced_hybrid_ME, modelica_conference_2021, simple_hybrid_CS, simple_hybrid_ME, mdpi_2022, juliacon_2023]
-        julia-version: ['1.8'] 
+        file-name: [growing_horizon_ME, modelica_conference_2021, simple_hybrid_CS, simple_hybrid_ME, mdpi_2022, juliacon_2023]
+        julia-version: ['1.9'] 
         julia-arch: [x64]
         experimental: [false]
 
@@ -44,7 +44,7 @@ jobs:
         env:
           FILE: examples/src/${{ matrix.file-name }}.ipynb
         run: |
-          jupyter nbconvert --ExecutePreprocessor.kernel_name="julia-1.8" --to notebook --inplace --execute ${{ env.FILE }}
+          jupyter nbconvert --ExecutePreprocessor.kernel_name="julia-1.9" --to notebook --inplace --execute ${{ env.FILE }}
           jupyter nbconvert --to script ${{ env.FILE }}
           jupyter nbconvert --to markdown ${{ env.FILE }}
 

Project.toml

@@ -1,13 +1,13 @@
 name = "FMIFlux"
 uuid = "fabad875-0d53-4e47-9446-963b74cae21f"
-version = "0.10.6"
+version = "0.11.0"
 
 [deps]
 Colors = "5ae59095-9a9b-59fe-a467-6f913c188581"
-DiffEqCallbacks = "459566f4-90b8-5000-8ac3-15dfb0a30def"
 DifferentiableEigen = "73a20539-4e65-4dcb-a56d-dc20f210a01b"
 DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa"
 FMIImport = "9fcbc62e-52a0-44e9-a616-1359a0008194"
+FMISensitivity = "3e748fe5-cd7f-4615-8419-3159287187d2"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 Optim = "429524aa-4258-5aef-a3af-852621145aeb"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
@@ -18,13 +18,13 @@ ThreadPools = "b189fb0b-2eb5-4ed4-bc0c-d34c51242431"
 
 [compat]
 Colors = "0.12.8"
-DiffEqCallbacks = "2.26.0"
 DifferentiableEigen = "0.2.0"
-DifferentialEquations = "7.8.0"
-FMIImport = "0.15.8"
-Flux = "0.13, 0.14"
+DifferentialEquations = "7.10.0 - 7.11"
+FMIImport = "0.16.2"
+FMISensitivity = "0.1.2"
+Flux = "0.13.0 - 0.14"
 Optim = "1.7.0"
-ProgressMeter = "1.7.0"
+ProgressMeter = "1.7.0 - 1.9"
 Requires = "1.3.0"
 ThreadPools = "2.1.1"
 julia = "1.6"

README.md

@@ -41,10 +41,12 @@ You can evaluate FMUs inside of your loss function.
 - building and training NeuralFMUs consisiting of multiple FMUs
 - building and training FMUINNs (PINNs)
 - different AD-frameworks: ForwardDiff.jl (CI-tested), ReverseDiff.jl (CI-tested, default setting), FiniteDiff.jl (not CI-tested) and Zygote.jl (not CI-tested)
+- use `Flux.jl` optimisers as well as the ones from `Optim.jl`
 - ...
 
 ## What is under development in FMIFlux.jl?
 - performance optimizations
+- multi threaded CPU training
 - improved documentation
 - more examples
 - FMI3 integration
@@ -62,6 +64,7 @@ To keep dependencies nice and clean, the original package [*FMI.jl*](https://git
 - [*FMIImport.jl*](https://github.com/ThummeTo/FMIImport.jl): Importing FMUs into Julia
 - [*FMIExport.jl*](https://github.com/ThummeTo/FMIExport.jl): Exporting stand-alone FMUs from Julia Code
 - [*FMICore.jl*](https://github.com/ThummeTo/FMICore.jl): C-code wrapper for the FMI-standard
+- [*FMISensitivity.jl*](https://github.com/ThummeTo/FMISensitivity.jl): Static and dynamic sensitivities over FMUs
 - [*FMIBuild.jl*](https://github.com/ThummeTo/FMIBuild.jl): Compiler/Compilation dependencies for FMIExport.jl
 - [*FMIFlux.jl*](https://github.com/ThummeTo/FMIFlux.jl): Machine Learning with FMUs (differentiation over FMUs)
 - [*FMIZoo.jl*](https://github.com/ThummeTo/FMIZoo.jl): A collection of testing and example FMUs

docs/make.jl

@@ -20,7 +20,7 @@ makedocs(sitename="FMIFlux.jl",
                     "Overview" => "examples/overview.md"
                     "Simple CS-NeuralFMU" => "examples/simple_hybrid_CS.md"
                     "Simple ME-NeuralFMU" => "examples/simple_hybrid_ME.md"
-                    "Advanced ME-NeuralFMU" => "examples/advanced_hybrid_ME.md"
+                    "Growing Horizon ME-NeuralFMU" => "examples/growing_horizon_ME.md"
                     "JuliaCon 2023" => "examples/juliacon_2023.md"
                     "MDPI 2022" => "examples/mdpi_2022.md"
                     "Modelica Conference 2021" => "examples/modelica_conference_2021.md"

docs/src/examples/overview.md

@@ -10,7 +10,7 @@ The examples show how to combine FMUs with machine learning ("NeuralFMU") and il
 ## Examples
 - [__Simple CS-NeuralFMU__](https://thummeto.github.io/FMIFlux.jl/dev/examples/simple_hybrid_CS/): Showing how to train a NeuralFMU in Co-Simulation-Mode.
 - [__Simple ME-NeuralFMU__](https://thummeto.github.io/FMIFlux.jl/dev/examples/simple_hybrid_ME/): Showing how to train a NeuralFMU in Model-Exchange-Mode.
-- [__Advanced ME-NeuralFMU__](https://thummeto.github.io/FMIFlux.jl/dev/examples/advanced_hybrid_ME/): Advanced training techniques for a ME-NeuralFMU.
+- [__Growing Horizon ME-NeuralFMU__](https://thummeto.github.io/FMIFlux.jl/dev/examples/growing_horizon_ME/): Growing horizon training technique for a ME-NeuralFMU.
 
 ## Advanced examples: Demo applications
 - [__JuliaCon 2023: Using NeuralODEs in real life applications__](https://thummeto.github.io/FMIFlux.jl/dev/examples/juliacon_2023/): An example for a NeuralODE in a real world engineering scenario.