# Trouble with NeuralPDE with ADAM optimizer on GPU

I’m trying to solve the dynamics of a massive particle on gravitational force by PINNs. The below code is inspired by an example in the documentation of NeuralPDE package. The problem is that the ADAM optimizer runs smoothly on CPU but on GPU (|>gpu) the loss gets NaN after the first iteration.

``````using NeuralPDE, Flux, ModelingToolkit, GalacticOptim, Optim, DiffEqFlux
import ModelingToolkit: Interval, infimum, supremum

@parameters t
@variables x(..), y(..)

Dt = Differential(t)
Dtt = Differential(t)^2

# 2D PDE
M = 60. # mass of the planet
X0 , Y0 = 0., 0. #position of the planet

eq  = [Dtt(x(t)) ~ -M/((x(t) - X0)^2 + (y(t) - Y0)^2)^1.5 * (x(t) - X0),
Dtt(y(t)) ~ -M/((x(t) - X0)^2 + (y(t) - Y0)^2)^1.5 * (y(t) - Y0)]

# Initial and boundary conditions
x0, y0 = 10., 10. # position of the test particle
vx0, vy0 = 1., -5.  #initial velocity of the test particle
bcs = [x(0.) ~ x0,
y(0.) ~ y0,
Dt(x(0.)) ~ vx0,
Dt(y(0.)) ~ vy0]

# Space and time domains
domains = [t ∈ Interval(0., 15.)]

inner = 25
chain = [FastChain(FastDense(1,inner,Flux.σ),
FastDense(inner,inner,Flux.σ),
FastDense(inner,inner,Flux.σ),
FastDense(inner,inner,Flux.σ),
FastDense(inner,1)) for _ in 1:2]

initθ = map(c -> Float64.(c), DiffEqFlux.initial_params.(chain)) |>gpu

strategy = GridTraining(0.01)
#strategy = QuasiRandomTraining(500)
discretization = PhysicsInformedNN(chain,
strategy;
init_params = initθ)

@named pde_system = PDESystem(eq,bcs,domains,[t],[x(t), y(t)])
prob = discretize(pde_system,discretization)

pde_inner_loss_functions = prob.f.f.loss_function.pde_loss_function.pde_loss_functions.contents
bcs_inner_loss_functions = prob.f.f.loss_function.bcs_loss_function.bc_loss_functions.contents

cb = function (p,l)
println("loss: ", l )
println("pde_losses: ", map(l_ -> l_(p), pde_inner_loss_functions))
println("bcs_losses: ", map(l_ -> l_(p), bcs_inner_loss_functions))
return false
end

#opt = BFGS()
res = GalacticOptim.solve(prob, opt;cb=cb,maxiters=5000)
``````

On CPU, does the loss NaN if you use Float32?

In the above code I changed the line `initθ = map(c -> Float64.(c), DiffEqFlux.initial_params.(chain)) |>gpu`
to `initθ = map(c -> Float32.(c), DiffEqFlux.initial_params.(chain))` and it worked without throwing NaN. peculiar. Open an issue. I would likely need to dig in line-by-line and compare between the two to see where the NaN first arises.

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You suggest to open an issue in which package? Do you think it’s a NeuralPDE issue or Flux?

If you have a little time I would like to ask a question about the problem at my hand. In the above code, I can minimize the loss by BFGS() but the result is a straight line. So the PINN can’t solve the Newtonian gravity problem to calculate the trajectory of the test particle in the gravitational field. I tried a lot but I couldn’t find where is the issue. Can you please give me a hint?

P/S: Happy birthday to you! I hope a lots of happiness and success for you.

NeuralPDE

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