@shashi could we easily get support for:

using OrdinaryDiffEq, ModelingToolkit

const N = 32
const xyd_brusselator = range(0,stop=1,length=N)
brusselator_f(x, y, t) = (((x-0.3)^2 + (y-0.6)^2) <= 0.1^2) * (t >= 1.1) * 5.
limit(a, N) = a == N+1 ? 1 : a == 0 ? N : a
function brusselator_2d_loop(du, u, p, t)
  A, B, alpha, dx = p
  alpha = alpha/dx^2
  @inbounds for I in Cartesia

Can we use a value of center that is outside of the domain? E.g. if we want to model a shell with a pointmass, can the value of center be at the origin, but ymin be at some large positive value?

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