[nerdponx]

Of course. "What do I fit this curve to" is a prerequisite to "what is the shape of this curve?"

You shouldn't feel the need to defend theory-based modeling against some imagined incursion from arrogant deep learning researchers. NNs work tremendously well in a few specific problem domains that we had no way to approach otherwise. Elsewhere, they're not much better than any other prediction algorithm. By the way XGBoost is curve-fitting, too.

[kxyvr]

I very much agree! Barring some kind of special intuition to the problem, I think ML are a fantastic tool for building models from empirical data. Even with intuition, sometimes they work as well. My core argument is that anthropomorphizing the algorithms has led to a great deal of confusion as to when we should or should not use these models. I often do computational modeling work with engineers and many of them are starting to eschew good, foundationaly sound models for ML not because they work better, in fact, on many of these problems they work far, far worse, but because good computational modeling is hard and it sounds like all they have to do with ML is teach the algorithms how physics works and how to be an engineer. Since they're good teachers, they should be able to teach the algorithm, right? In reality, it's still dirty, grinding computational modeling work. If we just called these models what they really are, empirical models, I think there'd be far less confusion as to when they should be used.