[patrickkidger]

> if that SciComp uses machine learning, I guess?

Not necessarily! It's perfectly possible (and quite common) to e.g. write down a traditional parameterised ODE, and then optimise its parameters via gradient descent. Compute the gradients wrt parameters using autodiff through the numerical ODE solver. All without a single neural network in sight! ;)

My usual spiel is that autodiff+autoparallel are really useful for any kind of numerical computation -- of which ML is a (popular, well funded) special case.

At least in my mini bubble, these kinds of "scipy but autodifferentiable" use-cases are fairly common.

> I have read a lot of your JAX issues and libraries ;-)

Haha, that's fun to hear though! Thank you for sharing that.

[cl3misch]

Ah, I think I was unclear. I specifically meant your reference to Equinox, because that seemed to me to be somewhat ML specific.

In general, I very much agree that "autodiff+autoparallel are really useful for any kind of numerical computation". And the use cases are also really common in my bubble. It's just that (imho) most people have not realized this.

[patrickkidger]

Ah right! Actually it's a good point, the Equinox readme/etc do tend to emphasise the ML use cases -- partly this is deliberate (go where the money is)! But I should probably tweak it to emphasise more general parameterised models.