I’m studying part of the 2D Swift Hohenberg model with an additive noise term, but I’m really new to the SDEProblems so I don’t know if my way to build the model is the best way to solve it.
I’ve take a look to the tutorial Stochastic Differential Equations · DifferentialEquations.jl and I’ve defined
function Add_noise(u,p,t) return fill(0.5,Nx*Ny) end
As the noise. Then
#Definition of the problem u0= rand(Nx,Ny) |> vec tspan = (0.0,35.0) prob =@time SDEProblem(F_sh,Add_noise,u0,tspan,p);
Where F_sh is
#Definition of the equation function F_sh(u, p, t) @unpack l , L1 = p return -L1 * u .+ (l .* u) end
And L1 is the SH operator.
Then I try to solve the problem using
#Solution of the problem sol =@time solve(prob,alg_hints=[:additive,:stiff],SRIW1(), save_everystep=false, save_start=false, saveat=[5,35])
But it seems not working very well because is slow, in particular increasing the resolution of the grid (Nx*Ny).
What kind of error I’m doing?
Thank you so much for the help, every comment is welcome!