diff --git a/docs/src/friction.md b/docs/src/friction.md
index e9a6f21..c59beb4 100644
--- a/docs/src/friction.md
+++ b/docs/src/friction.md
@@ -96,7 +96,7 @@ chain = Lux.Chain(
     Lux.Dense(10 => 10, Lux.mish, use_bias = false),
     Lux.Dense(10 => 1, use_bias = false)
 )
-nn = NeuralNetworkBlock(1, 1; chain = chain, rng = StableRNG(1111))
+@named nn = NeuralNetworkBlock(1, 1; chain = chain, rng = StableRNG(1111))
 
 eqs = [connect(model.nn_in, nn.output)
        connect(model.nn_out, nn.input)]
diff --git a/src/ModelingToolkitNeuralNets.jl b/src/ModelingToolkitNeuralNets.jl
index fd55d09..b8c9c17 100644
--- a/src/ModelingToolkitNeuralNets.jl
+++ b/src/ModelingToolkitNeuralNets.jl
@@ -17,7 +17,8 @@ include("utils.jl")
         chain = multi_layer_feed_forward(n_input, n_output),
         rng = Xoshiro(0),
         init_params = Lux.initialparameters(rng, chain),
-        eltype = Float64)
+        eltype = Float64,
+        name)
 
 Create an `ODESystem` with a neural network inside.
 """
@@ -26,7 +27,8 @@ function NeuralNetworkBlock(n_input = 1,
         chain = multi_layer_feed_forward(n_input, n_output),
         rng = Xoshiro(0),
         init_params = Lux.initialparameters(rng, chain),
-        eltype = Float64)
+        eltype = Float64,
+        name)
     ca = ComponentArray{eltype}(init_params)
 
     @parameters p[1:length(ca)] = Vector(ca)
@@ -39,8 +41,8 @@ function NeuralNetworkBlock(n_input = 1,
 
     eqs = [output.u ~ out]
 
-    @named ude_comp = ODESystem(
-        eqs, t_nounits, [], [p, T], systems = [input, output])
+    ude_comp = ODESystem(
+        eqs, t_nounits, [], [p, T]; systems = [input, output], name)
     return ude_comp
 end
 
diff --git a/test/lotka_volterra.jl b/test/lotka_volterra.jl
index 6b6f7a9..51ea9ef 100644
--- a/test/lotka_volterra.jl
+++ b/test/lotka_volterra.jl
@@ -44,7 +44,7 @@ end
 model = lotka_ude()
 
 chain = multi_layer_feed_forward(2, 2)
-nn = NeuralNetworkBlock(2, 2; chain, rng = StableRNG(42))
+@named nn = NeuralNetworkBlock(2, 2; chain, rng = StableRNG(42))
 
 eqs = [connect(model.nn_in, nn.output)
        connect(model.nn_out, nn.input)]