复杂动态系统中主导波动的分类和时空相关性
Cristina Caruso1, Martina Crippa1, Annalisa Cardellini2
1Department of Applied Science and Technology, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino 10129, Italy.
PNAS nexus
|February 19, 2025
概括
本研究引入了一种新的,可转移的方法,通过跟踪局部事件来分析复杂系统. 这种方法从单元轨迹中揭示了系统动态和现象,即使是未知的物理.
科学领域:
- 复杂系统分析 复杂系统分析
- 数据驱动的物理学
- 多尺度建模多尺度建模
背景情况:
- 复杂系统表现出来自局部重组的新兴行为,挑战传统的分析方法.
- 现有的策略往往需要系统特定的知识,缺乏可转移性.
- 了解局部事件传播对于预测集体现象至关重要.
研究的目的:
- 为复杂的动态系统提供一种通用,可转移和不可知论的分析方法.
- 为了使地方波动的检测,分类和时空相关性.
- 在没有先前知识的情况下,提供对系统物理的洞察力.
主要方法:
- 使用局部环境和邻居混合 (LENS) 和原子位置的时间平滑重叠 (TSOAP) 的两种组合.
- 从模拟或实验数据中分析构成单位的轨迹.
- 专注于局部波动及其时空相关性的抽象概念.
主要成果:
- 在多体动态系统中证明了局部波动的检测.
- 成功地分类了波动,并将它们与空间和时间相关联.
- 揭示了对地方和集体现象的出现和传播的见解.
结论:
- 本文所介绍的数据驱动方法为分析复杂系统提供了一种通用方法.
- 适用于未知物理的系统,从原子到宏观尺度.
- 为研究各种物理现象提供了新的视角.
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