diff --git a/test/batching.jl b/test/batching.jl
index e89b319f..19794d98 100644
--- a/test/batching.jl
+++ b/test/batching.jl
@@ -9,8 +9,8 @@ import Random
 Random.seed!(1234);
 
 t_start = 0.0
-t_step = 0.1
-t_stop = 150.0
+t_step = 0.01
+t_stop = 50.0
 tData = t_start:t_step:t_stop
 
 # generate training data
diff --git a/test/fmu_params.jl b/test/fmu_params.jl
index 3ee3dc47..7076c7f6 100644
--- a/test/fmu_params.jl
+++ b/test/fmu_params.jl
@@ -10,8 +10,8 @@ import Random
 Random.seed!(1234);
 
 t_start = 0.0
-t_step = 0.1
-t_stop = 15.0
+t_step = 0.01
+t_stop = 5.0
 tData = t_start:t_step:t_stop
 
 # generate training data
@@ -66,7 +66,7 @@ numStates = length(fmu.modelDescription.stateValueReferences)
 
 # the "Chain" for training
 net = Chain(FMUParameterRegistrator(fmu, p_refs, p),
-            x -> fmu(x=x, dx=:all)) # , fmuLayer(p))
+            x -> fmu(x=x, dx_refs=:all)) # , fmuLayer(p))
 
 optim = Adam(ETA)
 solver = Tsit5()
diff --git a/test/hybrid_CS.jl b/test/hybrid_CS.jl
index d42ff630..cf3c5c79 100644
--- a/test/hybrid_CS.jl
+++ b/test/hybrid_CS.jl
@@ -9,8 +9,8 @@ import Random
 Random.seed!(1234);
 
 t_start = 0.0
-t_step = 0.1
-t_stop = 15.0 
+t_step = 0.01
+t_stop = 5.0
 tData = t_start:t_step:t_stop
 
 # generate training data
diff --git a/test/hybrid_ME.jl b/test/hybrid_ME.jl
index 7edaa91d..b4f1abca 100644
--- a/test/hybrid_ME.jl
+++ b/test/hybrid_ME.jl
@@ -10,8 +10,8 @@ import Random
 Random.seed!(1234);
 
 t_start = 0.0
-t_step = 0.1
-t_stop = 15.0 
+t_step = 0.01
+t_stop = 5.0
 tData = t_start:t_step:t_stop
 
 # generate training data
diff --git a/test/hybrid_ME_dis.jl b/test/hybrid_ME_dis.jl
index 50aae01d..fe1d8953 100644
--- a/test/hybrid_ME_dis.jl
+++ b/test/hybrid_ME_dis.jl
@@ -10,8 +10,8 @@ import Random
 Random.seed!(5678);
 
 t_start = 0.0
-t_step = 0.1
-t_stop = 15.0 
+t_step = 0.01
+t_stop = 5.0
 tData = t_start:t_step:t_stop
 
 # generate training data
@@ -163,6 +163,7 @@ for i in 1:length(nets)
 
         # train it ...
         p_net = Flux.params(problem)
+        @test length(p_net) == 1
         
         @test problem !== nothing
 
@@ -175,7 +176,12 @@ for i in 1:length(nets)
 
         iterCB = 0
         lastLoss = losssum(p_net[1])
-        @info "[ $(iterCB)] Loss: $lastLoss"
+        @info "Start-Loss for net #$i: $lastLoss"
+
+        if length(p_net[1]) == 0
+            @info "The following warning is not an issue, because training on zero parameters must throw a warning:"
+        end
+
         FMIFlux.train!(losssum, p_net, Iterators.repeated((), NUMSTEPS), optim; cb=()->callb(p_net), gradient=GRADIENT)
 
         # check results
diff --git a/test/multi.jl b/test/multi.jl
index 45e8b37a..cec3c176 100644
--- a/test/multi.jl
+++ b/test/multi.jl
@@ -10,8 +10,8 @@ import Random
 Random.seed!(1234);
 
 t_start = 0.0
-t_step = 0.1
-t_stop = 15.0 
+t_step = 0.01
+t_stop = 5.0
 tData = t_start:t_step:t_stop
 
 # generate training data
diff --git a/test/multi_threading.jl b/test/multi_threading.jl
index 4bdadda9..06014b34 100644
--- a/test/multi_threading.jl
+++ b/test/multi_threading.jl
@@ -10,8 +10,8 @@ import Random
 Random.seed!(5678);
 
 t_start = 0.0
-t_step = 0.1
-t_stop = 15.0 
+t_step = 0.01
+t_stop = 5.0
 tData = t_start:t_step:t_stop
 
 # generate training data
diff --git a/test/optim.jl b/test/optim.jl
index 70649065..4f5007a1 100644
--- a/test/optim.jl
+++ b/test/optim.jl
@@ -11,8 +11,8 @@ import Random
 Random.seed!(1234);
 
 t_start = 0.0
-t_step = 0.1
-t_stop = 15.0 
+t_step = 0.01
+t_stop = 5.0
 tData = t_start:t_step:t_stop
 
 # generate training data
diff --git a/test/runtests.jl b/test/runtests.jl
index ff6beb2a..bc12d500 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -27,9 +27,9 @@ global x0 = [2.0, 0.0]
 
 # training data for pendulum experiment 
 function syntTrainingData(tData)
-    posData = cos.(tData)* 1.0
-    velData = sin.(tData)*-1.0
-    accData = cos.(tData)*-1.0
+    posData = cos.(tData*3.0)* 2.0
+    velData = sin.(tData*3.0)*-6.0
+    accData = cos.(tData*3.0)*-18.0
     return posData, velData, accData
 end
 
diff --git a/test/train_modes.jl b/test/train_modes.jl
index 44e62df6..8df4e24f 100644
--- a/test/train_modes.jl
+++ b/test/train_modes.jl
@@ -11,8 +11,8 @@ import Random
 Random.seed!(5678);
 
 t_start = 0.0
-t_step = 0.1
-t_stop = 15.0 
+t_step = 0.01
+t_stop = 5.0
 tData = t_start:t_step:t_stop
 
 # generate training data