A lot of science is done with GPUs ... in FORTRAN and C++. I believe an n body simulation in GPUs is what started the idea of using GPU for general computation.

A lot of science is done with GPUs ... in FORTRAN and C++. I believe an n body simulation in GPUs is what started the idea of using GPU for general computation. I would be extremely surprised if you could get similar or better predictions for the same amount of computation from an off the shelf CNN than from a carefully tuned physical model. (I would love to see counterexamples). Even then I would question how well it extrapolates; you generally have a very good idea of where your physics simulation will break down. I think your friend is right; the physics community can't afford to throw away as much code as the tech community. I wouldn't spend years building a simulation in tensorflow because it will likely be obsolete before I finished (leading to the same problems as FORTRAN but increasing the amount of dependencies). In any case, if it works well enough it's not going to be rewritten. Eventually ML will become part of physics simulations, but I don't think there will be easy wins in these well developed areas.