It's an approximator, right? I don't know about Astronomy but there are obvious use cases where an approximate result is "good enough" and even better than a precise result if it's significantly cheaper (or faster!) to get the approximation than to calculate the precise result.

In cases like this I'm always thinking of Reimann integrals and how I remember feeling my face scrunching up in distaste when they were first explained to us in class. It took a while for me to feel comfortable with the whole idea. I'm a very uh discrete kind of person.

As an aside, I consider the kind of work described in the article where a classic, symbolic system is essentially "compiled" into a neural net as one of the good forms of neuro-symbolic AI. Because it works and like I say there are important use cases where it beats just using the symbolic system.

Neuro-symbolic AI can often feel a bit like English cuisine where stuff is like bangers (i.e. sausages) and mash or a full English (breakfast), or a Sunday roast, where a bunch of disparate ingredients are prepared essentially independently and separately and then plonked on a plate all together. Most other cuisines don't work that way: you cook all the ingredients together and you get something bigger than the sum of the parts, a gestalt, if you like. Think e.g. of Greek gemista (tomatoes, bell peppers and occasionally zucchini and aubergines stuffed with rice) or French cassoulet (a bean stew with three different kinds of meat and a crusty top).

Lots of the neuro-symbolic stuff I've seen do it the English breakfast way: there's a neural net feeding its output to a symbolic system, rarely the other way around. But what the authors have done here, which others have also done, is to train a neural net on the output of a symbolic system, thereby basically "cooking" them together and getting the best of both worlds. Not yet a gestalt, as such, but close. Kind of like souvlaki with pitta (what the French call a "sandwich Grecque").