differential-equations

If I run the copy&paste examples, such as https://mtk.sciml.ai/stable/tutorials/acausal_components/ :

using ModelingToolkit, Plots, DifferentialEquations

@variables t
@connector function Pin(;name)
    sts = @variables v(t)=1.0 i(t)=1.0 [connect = Flow]
    ODESystem(Equation[], t, sts, []; name=name)
end

function Ground(;name)
    @named g = Pin()
    eqs = [g.v ~ 0]
    

https://arxiv.org/abs/2012.06684

@samuela is there code to share that could become a tutorial? I think it would be good to make one.

add in docs and in testset a Low-level API example with GPU

Hi,

I am pretty new to neurodiffeq, thank you very much for the excellent library.

I am interested in the way, and the computational speed, of computing partial derivatives w.r.t. the inputs.

Take forward ODE (1D, 1 unknown variable) solver for example, the input is x, a batch of coordinates, and the output of the neural network is y, the approximated solution of the PDE at these coo