This is one of the reasons current AI tech is so poor at learning physical world dynamics.
Relationships in the physical world are sparse, metastable graphs with non-linear dynamics at every resolution. And then we measure these dynamics using sparse, irregular sampling with a high noise floor. It is just about the worst possible data model for conventional AI stacks at a theoretical level.
It wouldn't surprise me if many interesting functions we'd like to approximate aren't continuous at all.