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极端天气事件的无监督发现,使用新兴组织的普遍表示
Adam Rupe1,2, Karthik Kashinath3,4, Nalini Kumar5
1Pacific Northwest National Laboratory, Richland, Washington 99352, USA.
Chaos (Woodbury, N.Y.)
|August 4, 2025
概括
本研究引入了一个新的框架,使用时空光来识别和描述复杂系统中新出现的组织. 这种方法在科学数据中成功地检测到诸如和极端天气事件等结构.
科学领域:
- 复杂系统科学 复杂系统科学
- 物理 物理学 物理
- 数据科学数据科学数据科学
背景情况:
- 在远离热力学平衡的系统中,自发的自我组织很常见.
- 在这些系统中识别新兴结构是具有挑战性的.
- 现有的方法缺乏对这些关键对象的普遍表示.
研究的目的:
- 开发一个理论基础的,数据驱动的框架来描述新兴组织.
- 引入局部因果状态作为捕捉有组织行为的方法.
- 展示框架在现实数据中发现和跟踪结构的能力.
主要方法:
- 利用时空光来表示信息传播.
- 定义局部因果状态作为光的预测等效类.
- 使用无监督的基于物理的机器学习和高性能计算.
主要成果:
- 成功识别和跟踪2D流体流中的,包括它们的电力规律衰变.
- 检测和跟踪极端天气事件,如风和大气河流.
- 在气候数据中发现了与极端降水相关的新型结构.
结论:
- 当地因果状态为新兴组织提供了普遍的代表性.
- 该框架提供了一种实用,数据驱动的方法来分析复杂的时空系统.
- 该方法在各种科学领域具有广泛的适用性.
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