Well, at least in physical oceanography there are a plethora of special cases the student and scholar are expected to remember. Sure, these are all corollaries of Navier-Stokes, but their peculiar evolution depends upon the boundary and other conditions of the ocean medium. There are presumably similar phenomena on "waterworlds" in the Solar System, and on the gas giants, but their boundary conditions are different, and, so, produce qualitatively different results. Another subfield is study of wave phenomena.
There are disciplines which rely primarily upon memory for their expertise, Medicine being one. But in Geophysics, theory seems elevated, but in practice and in the end, there are a large number of special cases which demand memorization. It is not surprising to me at all that machine learning is help make substantial progress here, because case analysis with hundreds of thousands of cases is precisely the kind of thing ML is good at. Nevertheless, I have heard ML dissed by some Big Names, and they have, to me, expressed how they wished people
and students approached them and the People Who Know These Things with greater humility. My take is that it is true priesthood. And I don't pretend I understand any of this, surely not as thoroughly as Paul or you, Dr Drake, or my friend, Ray Pierrehumbert. or Prof Mark Jacobson. But, frankly, with this kind of attitude, apart from Ray's great book on climate, or Mark's Atmospheric Modeling, why would I want to learn? There are so many other interesting fields which are approachable and more egalitarian. I'm not special, and I'm not pretending I can make any contribution here, but if I feel this way, why wouldn't students?