I tried to run NeuralPDE with GPU by the tutorial code in NeuralPDE.jl as follows:

```
using NeuralPDE, Lux, CUDA, Random
using Optimization
using OptimizationOptimisers
import ModelingToolkit: Interval
@parameters t x y
@variables u(..)
Dxx = Differential(x)^2
Dyy = Differential(y)^2
Dt = Differential(t)
t_min= 0.
t_max = 2.0
x_min = 0.
x_max = 2.
y_min = 0.
y_max = 2.
# 2D PDE
eq = Dt(u(t,x,y)) ~ Dxx(u(t,x,y)) + Dyy(u(t,x,y))
analytic_sol_func(t,x,y) = exp(x+y)*cos(x+y+4t)
# Initial and boundary conditions
bcs = [u(t_min,x,y) ~ analytic_sol_func(t_min,x,y),
u(t,x_min,y) ~ analytic_sol_func(t,x_min,y),
u(t,x_max,y) ~ analytic_sol_func(t,x_max,y),
u(t,x,y_min) ~ analytic_sol_func(t,x,y_min),
u(t,x,y_max) ~ analytic_sol_func(t,x,y_max)]
# Space and time domains
domains = [t ∈ Interval(t_min,t_max),
x ∈ Interval(x_min,x_max),
y ∈ Interval(y_min,y_max)]
# Neural network
inner = 25
chain = Chain(Dense(3,inner,Lux.σ),
Dense(inner,inner,Lux.σ),
Dense(inner,inner,Lux.σ),
Dense(inner,inner,Lux.σ),
Dense(inner,1))
strategy = GridTraining(0.05)
ps = Lux.setup(Random.default_rng(), chain)[1]
ps = ps |> Lux.ComponentArray |> gpu .|> Float64
discretization = PhysicsInformedNN(chain,
strategy,
init_params = ps)
@named pde_system = PDESystem(eq,bcs,domains,[t,x,y],[u(t, x, y)])
prob = discretize(pde_system,discretization)
symprob = symbolic_discretize(pde_system,discretization)
callback = function (p,l)
println("Current loss is: $l")
return false
end
res = Optimization.solve(prob,Adam(0.01);callback = callback,maxiters=2500)
```

And got the message:

```
UndefVarError: `ComponentArray` not defined
Stacktrace:
[1] getproperty(x::Module, f::Symbol)
@ Base .\Base.jl:31
[2] top-level scope
@ In[11]:44
```

I also installed `ComponentArrays.jl`

, but it still had this error. Please help me on how to fix it.

Thank you all.