Hong-Kun Zhang

Structure-aware learning of Poincaré return dynamics via symbolic decomposition.

A data-driven framework for approximating the first same-direction return time and the associated Poincaré map of chaotic flows is developed, and a symbolic residual-learning strategy based on finite itineraries of transverse section crossings is introduced.