Universal Approximation Theorems for Dynamical Systems with Infinite-Time Horizon Guarantees

Universal approximation theorems establish the expressive capacity of neural network architectures. For dynamical systems, existing results are limited to finite time horizons or systems with a globally stable equilibrium, leaving multistability and limit cycles unaddressed. We prove that Neural ODEs achieve $\epsilon$-$\delta$ closeness, i.e., trajectories within error $\epsilon$ except for initial conditions of measure $<\delta$, over the infinite time horizon $[0,\infty )$ for three target classes: (1) Morse-Smale systems (a structurally stable class) with hyperbolic fixed points, (2) Morse-Smale systems with hyperbolic limit cycles via exact period matching, and (3) systems with normally hyperbolic continuous attractors via discretization. We further establish a temporal generalization bound: $\epsilon$-$\delta$ closeness implies $L^p$ error $\le {\epsilon }^p+\delta \cdot D^p$ for all $t\ge 0$, bridging topological guarantees to training metrics. These results provide the first universal approximation framework for multistable infinite-horizon dynamics.