ode

Hi I would like to propose a better implementation for 'test_indices':

We can remove the unneeded np.array casting:

Cleaner/New:
test_indices = list(set(range(len(texts))) - set(train_indices))

Old:
test_indices = np.array(list(set(range(len(texts))) - set(train_indices)))

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]
    

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

We really should be testing each and every feature of Latexify and currently we are not.

Test generation is made easy by a macro that we supply:

using Latexify
@Latexify.generate_test latexify("x/y")

generates a test and puts it in your clipboard to be pasted:

@test latexify("x/y") == 
raw"$\frac{x}{y}$"

One just have to make sure that the test does ac

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

CLI already lints, but programmatic interface doesn't lint by default. Model linting should definitely be performed when import fails to avoid having to reimplement validation checks in amici and provide helpful error messages.