Universal Approximation of Maximal Lyapunov Functions with Anchored Neural Networks

Abstract

Maximal Lyapunov functions encode the entire domain of attraction of an asymptotically stable equilibrium, but preserving strict decrease under neural approximation is difficult because its margin vanishes at the equilibrium. For systems locally dominated by an asymptotically stable homogeneous vector field, we construct a continuously differentiable maximal target and an anchored, positivity-preserving neural family. We prove semiglobal universal approximation: strict neural Lyapunov functions and their first derivatives can approximate the target on nested invariant sublevel sets that exhaust the domain of attraction. We also provide directly verifiable conditions under which a candidate neural Lyapunov function can be formally certified, and illustrate the effectiveness of the proposed neural architecture through numerical examples.