准确的基于深度学习的过混沌动态,通过识别不稳定性而没有整体
Marc Bocquet1, Alban Farchi1, Tobias S Finn1
1CEREA, École des Ponts and EDF R&D, Île-de-France, France.
Chaos (Woodbury, N.Y.)
|September 30, 2024
概括
深度学习可以发现混乱系统的数据同化方案. 神经网络方法匹配集体卡尔曼波器的精度,不需要集体,优于变异方法.
科学领域:
- 大气科学 大气科学
- 动态系统 动态系统
- 机器学习 机器学习
背景情况:
- 数据同化 (DA) 对于分析混乱系统至关重要.
- 传统的DA方法,如集体卡尔曼过器和变异方法都有局限性.
- 深度学习提供了一种新的方法来增强DA.
研究的目的:
- 调查深度学习的使用,以发现数据同化方案.
- 专注于学习混乱动态的分析步骤的顺序DA.
- 将深度学习 DA 的性能与现有方法进行比较.
主要方法:
- 使用剩余卷积神经网络来学习分析步骤.
- 在已知动态的状态轨迹和观测上训练网络.
- 实验洛伦兹96动态,以时空混乱而闻名.
主要成果:
- 学习分析方案的准确性与最好的集体卡尔曼过器相提并论.
- 深度学习方法显著优于可变的DA替代方案.
- 即使在预测步骤中只有一个状态,也保持了高准确度.
结论:
- 深度学习可以有效地发现混乱系统的准确数据同化方案.
- 神经网络学会识别关键的动态扰动,而不是集体共变.
- 这表明网络学习了DA过程的基本原理作为一个随机动态系统.
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