估计高维ODE模型的动态和统计性质:洛伦茨'05型II模型的案例
Aljaž Pavšek1, Martin Horvat2, Juš Kocijan3
1Jozef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia.
Chaos (Woodbury, N.Y.)
|July 17, 2023
概括
本研究评估了噪声如何影响使用稀疏识别非线性动态 (SINDy) 估计的模型. SINDy模型在中等噪音下很好地捕捉系统动态,但随着噪音的增加,它会失去混乱的行为.
科学领域:
- 动态系统 动态系统
- 计算物理 计算物理
- 数据科学数据科学数据科学
背景情况:
- 模型性能通常通过预测准确度来评估.
- 评估估计模型对其源系统的统计和动态属性的忠实性至关重要.
- 非线性动力学的稀疏识别 (SINDy) 是一种关键算法,用于从时间序列数据中推导治理方程.
研究的目的:
- 调查噪音对使用SINDy.y估计模型的影响.
- 分析SINDy估计模型和原始动态系统的特性之间的差异.
- 了解噪音如何影响SINDy模型的统计和动态特征.
主要方法:
- 使用一个更高维的洛伦兹2005型II模型作为源系统.
- 将SINDy算法应用于时间序列数据,其中添加了不同水平的白色高斯噪声.
- 检查估计模型的动态性质,轨迹边界性和混乱水平.
- 在SINDy估计模型的自由参数上进行方差分析.
主要成果:
- 根据SINDy的估计,模型在特定的噪声范围内与源系统保持合理的动态属性对齐,避免不同的轨迹.
- 噪音水平的增加导致SINDy估计模型的混乱动态减少.
- 差异分析揭示了SINDy模型中相同的普通微分方程组件内的参数之间的显著相关性.
结论:
- SINDy模型可以有效地表示复杂的动态系统,但它们的保真性对噪声水平敏感.
- 噪音可以抑制估计模型中的混乱行为,影响其动态现实性.
- 在SINDy模型中的参数相关性为基础动态系统的结构提供了洞察力.
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