基于数据的建模和预测混沌动态的惯性多元体构建为光谱子多元体的混沌动态
Aihui Liu1, Joar Axås1, George Haller1
1Institute for Mechanical Systems, ETH Zürich, Leonhardstrasse 21, 8092 Zürich, Switzerland.
Chaos (Woodbury, N.Y.)
|March 26, 2024
概括
我们开发了一种数据驱动的方法,使用光谱子变量 (SSM) 来简化混乱系统. 这种方法准确地预测混乱的动态和从缩小模型的长期统计特征.
科学领域:
- 非线性动力学是一种非线性动力学.
- 混沌理论 混沌理论
- 数据驱动建模数据驱动建模
背景情况:
- 高维的混乱系统是复杂的分析.
- 传统的方法很难捕捉到基本的动态.
- 低维的吸引力控制着混乱的行为.
研究的目的:
- 介绍一个数据驱动的方法来减少混乱系统的维度.
- 引入光谱亚多元 (SSM) 作为模拟混乱的可解释工具.
- 证明基于SSM的缩小模型的预测准确性.
主要方法:
- 使用光谱子多元 (SSM) 识别低维惯性多元.
- 从高维的混乱数据中提取减少的动态.
- 对各种混乱系统的方法进行验证 (洛伦茨,罗斯勒,达芬,库拉莫托-西瓦辛斯基).
主要成果:
- SSM准确地预测了几次Lyapunov时间的混乱动态.
- 缩小模型复制关键的统计特征,如莱普诺夫指数和概率分布.
- 该方法成功地使用非强制数据预测了强制曲光束的混乱反应.
结论:
- 频谱子多元提供了一个可解释和有效的方法来减少混乱系统的维度.
- 这种数据驱动的方法提供了准确的短期预测,并捕获了长期的统计特征.
- 该技术在模拟和预测复杂的非线性现象方面表现出有希望.
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