I see a lot of problems wit such approaches. Firstly, the results may look similar but they certainly are not the same so using them to get an idea/a hunch is o

I see a lot of problems wit such approaches. Firstly, the results may look similar but they certainly are not the same so using them to get an idea/a hunch is ok but using them directly for scientific purposes is questionable. Secondly, NN often extrapolate badly, at best they are not reliable because we don’t know why/how they work. Object recognition is the prominent counter-example here. Even with state-of-the art networks while the performance is usually very good, they sometimes surprised us with “idiotic” errors and could be easily confused by covering/over-blending part of the picture or simply changing the scale/perspective. We’re still far far way from robust networks so I’d be cautious about using such techniques. Besides, one important goal of simulations is to help validate theories. If the simulation result is faked, it might help to demonstrate the idea but not the theory itself.