I’m trying to fit a simple Von Bertalanffy curve to some data using DiffEqFlux but one of the parameters (`init_p[3]`

) does not change with each iteration. I suspect it might have something to do with the step function inside `prod_f()`

, which I am using to force a time delay before the curve begins rising. There is probably something I am not understanding about how the parameters are being accessed during the training process. Any help is appreciated!

Here is the code:

```
using DifferentialEquations, Flux, DiffEqFlux, Plots
function prod_f(du,u,p,t)
L = u[1]
#Von Bertalanffy curve
k,Lmax,delay = p
if t >= delay
d = 1
else
d = 0
end
du[1] = k*(Lmax - L)*d
end
u0 = [0.0]
tspan = (1.0,500)
true_p = [0.01,25.0,100.0]
prob1 = ODEProblem(prod_f,u0,tspan,true_p,saveat = 1.0)
sol1 = solve(prob1,Tsit5())
plot(sol1,label = "")
x = [14,14,15,17,17,18,18,19,19,20,20,21,22,23,24,25,26,27,28,29,30,
32,34,35,36,38,39,40,41,42,45,46,47,48,49,50,51,52,53,55,56,58,59,62].*7
data = VectorOfArray([(sol1(x[i]) + .01randn(1)) for i in 1:length(x)])
data = convert(Array,data)
plot!(x,data',seriestype = :scatter)
init_p = [0.02,28.0,90.0]
function predict_adjoint(p)
Array(concrete_solve(prob1,Tsit5(),u0,p,saveat=x))
end
function loss_adjoint(p)
prediction = predict_adjoint(p)
loss = sum(abs2,data .- prediction)
loss,prediction
end
cb = function (p,l,pred) #callback function to observe training
# println(p)
# display(l)
# using `remake` to re-create our `prob` with current parameters `p`
# display(plot(solve(remake(prob1,p=p),Tsit5(),saveat = x)))
return false # Tell it to not halt the optimization. If return true, then optimization stops
end
# Display the ODE with the initial parameter values.
cb(init_p,loss_adjoint(init_p)...)
res = DiffEqFlux.sciml_train(loss_adjoint, init_p, ADAM(0.05), cb = cb,
maxiters = 200)
plot!(solve(remake(prob1,p=res.minimizer),Tsit5(),saveat=0.0:0.1:500.0),
label = "",linewidth = 3)
plot!(x,data',seriestype = :scatter)
```