I don't know a huge amount about neurology (or neural nets), but... Adult humans seem to learn faster when there is some combination of theory, examples, and e

I don't know a huge amount about neurology (or neural nets), but... Adult humans seem to learn faster when there is some combination of theory, examples, and experience (aka feedback). I'm a scientist and I have very little interest in neural nets for science because it contorts away the kind of equation-based systematics that we rely on to understand our world. The theory component is missing. I'm more interested in expert system-type AI, where the learning may help us infer more about the world in the systematic way that scientists try to grapple with it--logical rules and frameworks, physics equations, statistics, etc. But these seem to be far less effective or efficient than ANNs, at least as currently implemented. That being said, I did see a great talk last week on using ANNs to accelerate viscoelasticity calculations--those sorts of things are definitely valuable for science by reducing the time required for simulations by orders of magnitude. And some of the discussion after the talk had to do with how a graph of biases and weights is, philosophically and practically, a fundamentally different way of representing the problem and its solution than a set of differential equations. There is no doubt that it's a useful way of solving the calculations, but it's unclear how we can build upon those weights and biases in a theoretically-meaningful way. How do we learn from machine learning?