It (along with Stokes’ theorem (they’re actually the same theorem in different dimensions)) helps yield Maxwell’s equations; specifically, if you want to change the flux of the electric field through a surface (right hand side), you need to change the amount of charge it contains (the source of the divergence on the left hand side). In other words, if you have the same charge contained by a surface, it will have the same flux going through it, which means you can change the surface however you wish and the math will still be the same. Physicists use this to reduce some complex problems into problems on a sphere or a box—objects with nice, easily calculable symmetries.
Deep learning doesn’t stop at llms. Honestly, language isn’t a great use case for them. They are—by nature—statistics machines, so if you have a fuck load of data to crunch, they can work very quickly to find patterns. The patterns might not always be correct, but if they are easy to check, then it might be faster to use them and modify the result compared to doing it all yourself.
I don’t know what this person does, though, and it will depend on the specifics of the situation for how they are used.