Isn’t it more technically correct to state that a “regular NN” could learn to extrapolate this in theory, but is so unlikely to do so that the probability might as well be zero?

PINNs are basically universal function approximators that have additional knowledge about physics-based constraints imposed, so it’s not surprising and shouldn’t be taken as an “dig” on “regular NNs” that they can better decide what solutions may make sense and are admissible vs. something that is basically of an “equivalent” architecture and design but without any knowledge of physics encoded in to regularize it.