In my experience, papers which propose numerical solutions cover in great detail the methodology (which relates to some underlying physical phenomena), and also explain boundary conditions to their solutions. In ML/DL papers, they tend to go over the network architecture in great detail as the network construction is the methodology. But the problem I think is that there’s a disconnect going from raw data to features to outputs. I think physics informed ML models are trying to close this gap somewhat.

well numerical models have to come up with some model that explains how relevant observables behave. With AI you don't even build the model that explains the system physically and mathematically, let alone the solution.

It is basically like having Newton's Equations vs an AI that produces coordinates with respect to time (and possibly many other inputs we thought were relevant but weren't because we don't know the model) given initial conditions and force fields.