Fitting a dynamic system with an exogenous input (nonhomogenous neural ode) via DiffEqFlux


I´m trying to fit a dynamic system (a hammerstein system, implemented below) with an exogenous input via DiffEqFlux, but actually I don´t know how to put the input excitation into the n-ode-problem. I first tried to follow the example:

Example 3: Solving Nonhomogeneous Equations using Parameterized Functions,

but I wasn´t able to transfer it to my application. I posted below my attempt. Maybe someone could tell me what I´m doing wrong?

# intialization
using DifferentialEquations, Flux, Optim, DiffEqFlux, DiffEqSensitivity, Plots

# excitation of the system
no_samples = 100
sample_period = 0.1
tsteps = collect(sample_period:sample_period:(no_samples-1)*sample_period)
tspan = (Float32(tsteps[1]), Float32(tsteps[end]))
ex = t->sin(t)
u = ex.(tsteps)

# system
f(x) = (atan(8.0 * x - 4.0) + atan(4.0)) / (2.0 * atan(4.0))
function hammerstein_system(u::Array{Float64})
    y= zeros(size(u))
    for k in 2:length(u)
        y[k] = 0.2 * f(u[k-1]) + 0.8 * y[k-1]
    return y

# system output
y = hammerstein_system(ex.(tsteps))

# model setup
nn_model = FastChain(FastDense(1,50, sigmoid), FastDense(50, 1))

#intial parameter of the model
p_model = initial_params(nn_model)
function predict_neuralode(p)
  Array(prob_neuralode(u0, p))

function dudt(du, u, p, t)
    du[1] = nn_model(ex(t))[1]

prob_neuralode = NeuralODE(dudt, tspan, Tsit5(), saveat = tsteps)

prob_neuralode(0.0, p_model)

Optimal control is essentially learning an input excitation, so is an example you might want to look at. Basically, you just pass the function, not much else to it.

Thanks. I will try it, but in my point of view the main difference is, that I would like to excitate the system with an excitation signal and in the example the signal is the optimization target. So it is generated by the optimization in contrast to that I would like to specify the signal externally and excitate the system and n-ode with the prepared signal. The reason is that exciatation signals like chirp, aprbs or multisine are useful to gather as much as possible information of the system.

Okay, I thought, that I would evaluate ex = t->sin(t) at the specific time point and so pass the value of it to the nn. I haven’t quite got behind DiffEqFlux’s user interface yet