Why neural networks with flow structure can learn anything

Flow-based neural networks—those built like continuous differential equations—can theoretically approximate any function or operator, including infinite-dimensional ones like those needed for physics simulations. The authors prove this universal approximation result for the first time in the infinite case, and show how two different internal structures (composition and separation) mathematically connect to practical architectures like ResNets and plain networks through discretization.