库普曼运算子和通过符号动态测量最大
Connor Kennedy1, John Kaushagen2, Hong-Kun Zhang3
1Department of Mathematics, Brandeis University, Waltham, Massachusetts 02453, USA.
Chaos (Woodbury, N.Y.)
|February 17, 2026
概括
符号式扩展动态模式分解 (EDMD) 现在可以估计库普曼运算符,而不需要不变量. 这种方法还可以近似地测量某些动态系统的最大值.
科学领域:
- 动态系统理论 动态系统理论
- 埃尔戈迪克理论 埃尔戈迪克理论
- 数据驱动科学数据驱动科学
背景情况:
- 符号式扩展动态模式分解 (EDMD) 之前使用不变量和已知的分区估计了库普曼运算符.
- 限制包括要求先前了解系统属性的要求.
研究的目的:
- 通过消除对不变措施的需求,推进象征性的EDMD.
- 证明该方法近似测量最大 (MME).
主要方法:
- 开发了一个新的象征性EDMD框架,不需要对不变量测量的知识.
- 证明该方法产生了一系列测量方法,接近马尔科夫或索菲克符号转移的MME.
- 将该方法应用于Liverani-Saussol-Vaienti地图,非马尔科夫断片线性地图和阿诺德猫地图.
主要成果:
- 成功估计了测试动态系统的光谱数据和MME.
- 在不同系统类型中证明了象征性EDMD方法的稳定性.
- 引入了新的数据驱动技术来估计象征性圆柱体集,特别是用于猫地图.
结论:
- 增强的象征性EDMD方法是分析动态系统的强大工具,不需要对不变量措施的预先了解.
- 该方法提供了一个数据驱动的方法,以近似MME,推进理论和计算研究在ergodic理论.
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