Persistent Symplectic Homology for Topological Data Analysis of Dynamical Systems

We introduce persistent symplectic homology as a new tool for topological data analysis, enabling the characterization of complex dynamics in high-dimensional phase spaces. This method provides robust invariants for detecting critical transitions and bifurcations, with potential applications in climate modeling and biological systems analysis.