diff --git a/test/NNPDE_tests_gpu.jl b/test/NNPDE_tests_gpu.jl
index 8f33efe716..4a2896a84d 100644
--- a/test/NNPDE_tests_gpu.jl
+++ b/test/NNPDE_tests_gpu.jl
@@ -25,7 +25,7 @@ eq = Dθ(u(θ)) ~ θ^3 + 2.0f0 * θ + (θ^2) * ((1.0f0 + 3 * (θ^2)) / (1.0f0 +
                 u(θ) * (θ + ((1.0f0 + 3.0f0 * (θ^2)) / (1.0f0 + θ + θ^3)))
 
 # Initial and boundary conditions
-bcs = [u(0.0) ~ 1.0f0]
+bcs = [u(0.f0) ~ 1.0f0]
 
 # Space and time domains
 domains = [θ ∈ Interval(0.0f0, 1.0f0)]
@@ -85,7 +85,7 @@ chain = Flux.Chain(Dense(2, inner, Flux.σ),
                    Dense(inner, inner, Flux.σ),
                    Dense(inner, inner, Flux.σ),
                    Dense(inner, inner, Flux.σ),
-                   Dense(inner, 1)) |> gpu
+                   Dense(inner, 1)) |> gpu |> f64
 
 strategy = NeuralPDE.StochasticTraining(500)
 discretization = NeuralPDE.PhysicsInformedNN(chain,
@@ -138,7 +138,7 @@ chain = Flux.Chain(Dense(2, inner, Flux.σ),
                    Dense(inner, inner, Flux.σ),
                    Dense(inner, inner, Flux.σ),
                    Dense(inner, inner, Flux.σ),
-                   Dense(inner, 1)) |> gpu
+                   Dense(inner, 1)) |> gpu |> f64
 
 strategy = NeuralPDE.QuasiRandomTraining(500; #points
                                          sampling_alg = SobolSample(),
@@ -205,7 +205,7 @@ chain = Flux.Chain(Dense(3, inner, Flux.σ),
                    Dense(inner, inner, Flux.σ),
                    Dense(inner, inner, Flux.σ),
                    Dense(inner, inner, Flux.σ),
-                   Dense(inner, 1)) |> gpu
+                   Dense(inner, 1)) |> gpu |> f64
 
 strategy = NeuralPDE.GridTraining(0.05)
 discretization = NeuralPDE.PhysicsInformedNN(chain,
diff --git a/test/NNPDE_tests_gpu_Lux.jl b/test/NNPDE_tests_gpu_Lux.jl
index 6055673a73..6dfbe3aac1 100644
--- a/test/NNPDE_tests_gpu_Lux.jl
+++ b/test/NNPDE_tests_gpu_Lux.jl
@@ -1,7 +1,7 @@
 using Lux, ComponentArrays, OptimizationOptimisers
 using Test, NeuralPDE
 using Optimization
-using CUDA, QuasiMonteCarlo
+using LuxCUDA, QuasiMonteCarlo
 import ModelingToolkit: Interval, infimum, supremum
 
 using Random
@@ -12,7 +12,7 @@ callback = function (p, l)
     return false
 end
 CUDA.allowscalar(false)
-#const gpuones = cu(ones(1))
+const gpud = gpu_device()
 
 ## ODE
 println("ode")
@@ -41,7 +41,7 @@ chain = Chain(Dense(1, inner, Lux.σ),
               Dense(inner, 1))
 
 strategy = NeuralPDE.GridTraining(dt)
-ps = Lux.setup(Random.default_rng(), chain)[1] |> ComponentArray |> gpu .|> Float64
+ps = Lux.setup(Random.default_rng(), chain)[1] |> ComponentArray |> gpud
 discretization = NeuralPDE.PhysicsInformedNN(chain,
                                              strategy;
                                              init_params = ps)
@@ -90,7 +90,7 @@ chain = Lux.Chain(Dense(2, inner, Lux.σ),
                   Dense(inner, 1))
 
 strategy = NeuralPDE.StochasticTraining(500)
-ps = Lux.setup(Random.default_rng(), chain)[1] |> ComponentArray |> gpu .|> Float64
+ps = Lux.setup(Random.default_rng(), chain)[1] |> ComponentArray |> gpud .|> Float64
 discretization = NeuralPDE.PhysicsInformedNN(chain,
                                              strategy;
                                              init_params = ps)
@@ -148,7 +148,7 @@ strategy = NeuralPDE.QuasiRandomTraining(500; #points
                                          sampling_alg = SobolSample(),
                                          resampling = false,
                                          minibatch = 30)
-ps = Lux.setup(Random.default_rng(), chain)[1] |> ComponentArray |> gpu .|> Float64
+ps = Lux.setup(Random.default_rng(), chain)[1] |> ComponentArray |> gpud .|> Float64
 discretization = NeuralPDE.PhysicsInformedNN(chain,
                                              strategy;
                                              init_params = ps)
@@ -213,7 +213,7 @@ chain = Lux.Chain(Dense(3, inner, Lux.σ),
                   Dense(inner, 1))
 
 strategy = NeuralPDE.GridTraining(0.05)
-ps = Lux.setup(Random.default_rng(), chain)[1] |> ComponentArray |> gpu .|> Float64
+ps = Lux.setup(Random.default_rng(), chain)[1] |> ComponentArray |> gpud .|> Float64
 discretization = NeuralPDE.PhysicsInformedNN(chain,
                                              strategy;
                                              init_params = ps)