further adjustments

Author: ThummeTo

examples/src/growing_horizon_ME.ipynb

@@ -641,6 +641,8 @@
    "outputs": [],
    "source": [
     "# train\n",
+    "paramsNet = Flux.params(neuralFMU)\n",
+    "\n",
     "optim = Adam()\n",
     "FMIFlux.train!(lossSum, neuralFMU, Iterators.repeated((), 1000), optim; cb=()->callb(paramsNet)) "
    ]

examples/src/juliacon_2023.ipynb

@@ -465,10 +465,13 @@
     "                  gates,                              # compute resulting dx from ANN + FMU\n",
     "                  dx -> cacheRetrieve(1:4, dx))       # stack together: dx[1,2,3,4] from cache + dx[5,6] from gates\n",
     "\n",
+    "    solver = Tsit5()\n",
+    "    \n",
     "    # new NeuralFMU \n",
     "    neuralFMU = ME_NeuralFMU(f,                 # the FMU used in the NeuralFMU \n",
     "                             model,             # the model we specified above \n",
     "                             (tStart, tStop),   # a default start ad stop time for solving the NeuralFMU\n",
+    "                             solver;\n",
     "                             saveat=tSave)      # the time points to save the solution at\n",
     "    neuralFMU.modifiedState = false             # speed optimization (NeuralFMU state equals FMU state)\n",
     "    \n",

examples/src/juliacon_2023_helpers.jl

@@ -8,6 +8,7 @@ import FMIFlux: roundToLength
 import FMIZoo: movavg
 
 import FMI: FMU2Solution
+import FMI.DifferentialEquations: Tsit5
 import FMIZoo: VLDM, VLDM_Data
 
 function fmiSingleInstanceMode(fmu::FMU2, mode::Bool)

examples/src/mdpi_2022.ipynb

@@ -108,6 +108,7 @@
         "using FMIFlux.Flux  # Machine Learning in Julia\n",
         "\n",
         "import FMI.DifferentialEquations: Tsit5     # import the Tsit5-solver\n",
+        "import FMI: FMU2Solution\n",
         "using JLD2                                  # data format for saving/loading parameters\n",
         "\n",
         "# plotting\n",

src/neural.jl

@@ -1287,7 +1287,10 @@ function (nfmu::ME_NeuralFMU)(x_start::Union{Array{<:Real}, Nothing} = nfmu.x0,
     prob = ODEProblem{true}(ff, nfmu.x0, nfmu.tspan, p)
 
     if isnothing(sensealg)
-        if isimplicit(solver)
+        if !isnothing(solver)
+            @assert false "currently no solver specified!"
+            
+        elseif isimplicit(solver)
             @assert !(alg_autodiff(solver) isa AutoForwardDiff) "Implicit solver using `autodiff=true` detected for NeuralFMU.\nThis is currently not supported, please use `autodiff=false` as solver keyword.\nExample: `Rosenbrock23(autodiff=false)` instead of `Rosenbrock23()`."
 
             logWarning(nfmu.fmu, "Implicit solver detected for NeuralFMU.\nContinuous adjoint method is applied, which requires solving backward in time.\nThis might be not supported by every FMU.", 1)