I’m trying to do some parameter estimation of parameters in a set of coupled (stiff) differential equations by using some limited data and found out about Julia’s ODE suite.

Conv((2,2), 1=>16, relu),
x -> maxpool(x, (2,2)),
Conv((2,2), 16=>8, relu),
x -> maxpool(x, (2,2)),
x -> reshape(x, :, size(x, 4)),
x -> solve(prob,Tsit5(),u0=x,saveat=0.1)[1,:],
Dense(288, 10), softmax) |> gpu

In what situations would you say this method would be more relevant than just the example you linked to? Or is it a case of iterating over different methods and these two are examples of the different methods?

I don’t understand the question. DiffEqFlux is just a neural network architecture. It uses SciMLSensitivity to train the neural network architectures. If you just care about the training process, i.e. parameter estimation, then look at SciMLSensitivity.