线性反向建模方法用于估计在无记忆和持久噪声下周期静止过程中的梁 - 基曼信息流
Justin Lien1, Hiroyasu Ando2, Yong-Yub Kim3
1Tohoku University, Mathematical Institute, Sendai, Japan.
Physical review. E
|December 23, 2025
概括
我们介绍了一种新的数据驱动方法,它结合了Liang-Kleeman (LK) 信息流和线性反向建模 (LIM) 来量化因果关系和系统动态. 这个框架揭示了转移,并提供了关于海洋变化的见解.
科学领域:
- 地球和环境科学 地球和环境科学
- 海洋学 海洋学 海洋学
- 气候科学 气候科学
背景情况:
- 梁-克利曼 (LK) 信息流量量化了变量之间的因果关系.
- 线性反向建模 (LIM) 从输入数据研究系统动态.
研究的目的:
- 将LK信息流和LIM统一到一个数据驱动的框架 (LIM-LK).
- 估计信息流,量化因果关系和系统动态.
- 分析从环境到系统的转移.
主要方法:
- 开发了LIM-LK框架,一种数据驱动的方法.
- 应用LIM以从观测数据中估计LK信息流.
- 状态变量和转移之间的量化因果关系.
主要成果:
- LIM-LK框架成功地估计了信息流,并将因果关系与系统动态联系起来.
- 从环境中通过随机强迫转移量化的.
- 已被证明适用于太平洋 - 印度洋相互作用.
结论:
- 统一的LIM-LK框架为复杂系统提供因果和动态洞察力.
- 为分析海洋变化和季节性调节提供了一种新的方法.
- 强调在数据驱动的建模中整合因果关系和动态的重要性.
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