Hi.

I’m trying to use NeuralPDE to approximate a solution to a PDE containing both Neural nets and functions. Something goes wrong, can you help ? I am new to NeuralPDE, so it might just be a newbie error.

Here is the code:

```
import ModelingToolkit: Interval
@parameters x,y,z
L=5
domains = [x ∈ Interval(-L, +L),
y ∈ Interval(-L, +L),
z ∈ Interval(-L, +L)]
@variables G(..)
Fx0(x,y,z)= -sin(atan(y,x))*exp(-(sqrt(x*x+y*y)-2.0)^2-z*z)
Fy0(x,y,z)= cos(atan(y,x))*exp(-(sqrt(x*x+y*y)-2.0)^2-z*z)
Fz0(x,y,z)= 0.0
Dx = Differential(x)
Dy = Differential(y)
Dz = Differential(z)
eqs0=[Dy(Fz0(x,y,z))-Dz(Fy0(x,y,z))-Dx(G(x,y,z))~0,
Dz(Fx0(x,y,z))-Dy(Fz0(x,y,z))-Dy(G(x,y,z))~0,
Dx(Fy0(x,y,z))-Dy(Fx0(x,y,z))-Dz(G(x,y,z))~0]
bcs0 = [G(-L,y,z)~G(L,y,z), G(x,-L,z)~G(x,L,z),G(x,y,-L)~G(x,y,L),
Dx(G(-L,y,z))~Dx(G(L,y,z)), Dy(G(x,-L,z))~Dy(G(x,L,z)),Dz(G(x,y,-L))~Dz(G(x,y,L))]
input_ = length(domains)
n = 15
chain = Lux.Chain(Dense(input_, n, Lux.asinh), Dense(n, n, Lux.asinh), Dense(n, 1))
strategy = QuadratureTraining()
discretization = PhysicsInformedNN(chain, strategy)
@named pdesystem = PDESystem(eqs0, bcs0, domains, [x,y,z], G(x,y,z))
prob = discretize(pdesystem, discretization)
sym_prob = symbolic_discretize(pdesystem, discretization)
pde_inner_loss_functions = sym_prob.loss_functions.pde_loss_functions
bcs_inner_loss_functions = sym_prob.loss_functions.bc_loss_functions
callback = function (p, l)
print("loss: ", l)
print(" pde: ", map(l_ -> l_(p), pde_inner_loss_functions))
println(" bcs: ", map(l_ -> l_(p), bcs_inner_loss_functions))
return false
end
res = Optimization.solve(prob, Adam(0.5); callback = callback, maxiters = 5)
```

I get the following output:

```
loss: NaN pde: [4.9889631620980905, 4.507107273136388, NaN] bcs: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
loss: NaN pde: [NaN, NaN, NaN] bcs: [NaN, NaN, NaN, NaN, NaN, NaN]
loss: NaN pde: [NaN, NaN, NaN] bcs: [NaN, NaN, NaN, NaN, NaN, NaN]
loss: NaN pde: [NaN, NaN, NaN] bcs: [NaN, NaN, NaN, NaN, NaN, NaN]
loss: NaN pde: [NaN, NaN, NaN] bcs: [NaN, NaN, NaN, NaN, NaN, NaN]
loss: Inf pde: [4.9889631620980905, 4.507107273136388, NaN] bcs: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]```
```