Unfortunately no. The GAN always need to be in balance and contention with the generator. You can swap out the discriminator later, but you also got to make sure your discriminator is able to identify these errors. And ML models aren't the best at noticing small details. And since they too don't understand physics, there is no reason to believe that they will encode such information, despite every image in real life requiring consistency. Also remember that there is a learning trajectory, and most certainly these small details are not learned early on in networks. The problem is that this information is post hoc trivial to identify errors, but it isn't a priori. It is also easy for you because you know physics innately and can formulate causal explanations.