从持久性推断混沌流的符号动力学
Inferring symbolic dynamics of chaotic flows from persistence
摘要：
我们引入“状态空间持久性分析”来推导从高维混沌吸引子获得的时间序列数据的符号动力学。为此，我们适应的拓扑数据分析技术被称为持久的同源性的混沌轨道和周期轨道的状态空间投影的表征。状态空间持久性分析通过比较混沌轨道和周期轨道的形状，量化了混沌轨道段和周期轨道的形状相似性。我们证明了它的三维Rössler系统和30维离散的Kuramoto-Sivashinsky偏微分方程在$(1+1)$维的方法。
英文摘要：
We introduce "state space persistence analysis" for deducing the symbolic dynamics of time series data obtained from high-dimensional chaotic attractors. To this end, we adapt a topological data analysis technique known as persistent homology for the characterization of state space projections of chaotic trajectories and periodic orbits. By comparing the shapes along a chaotic trajectory to those of the periodic orbits, state space persistence analysis quantifies the shape similarity of chaotic trajectory segments and the periodic orbits. We demonstrate the method by applying it to the three-dimensional Rössler system and a thirty-dimensional discretization of the Kuramoto--Sivashinsky partial differential equation in $(1+1)$ dimensions.
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