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学习过渡到极端事件使用储库计算的学习
Ajit Mahata1, S Leo Kingston2,3, Subrata Ghosh1
1Technical University of Lodz, Division of Dynamics, Stefanowskiego 1/15, 90-924 Lodz, Poland.
Physical review. E
|December 23, 2025
概括
这项研究使用储库计算机器学习来预测动态系统中的极端事件. 该方法准确地预测事件幅度和过渡点,保持统计属性.
科学领域:
- 非线性动力学是一种非线性动力学.
- 机器学习是机器学习.
- 复杂的系统复杂的系统.
背景情况:
- 动态系统中的极端事件很难预测,因为时间不规则和幅度大.
- 准确预测振幅和时间仍然是一个重大挑战.
研究的目的:
- 应用储水库计算机器学习来预测范式动态系统中的极端事件.
- 评估机器学习在预测事件过渡,振幅和统计属性的有效性.
- 为了确定预测的最佳输入参数,使用预测地平线和平均平方误差等指标.
主要方法:
- 使用部分或完整的系统变量信息,采用了水库计算机器学习.
- 该方法在强迫的Liénard系统,结合的FitzHugh-Nagumo模型和隐藏的吸引力模型上进行了测试.
- 来自强制Liénard电路的数值数据和实时实验数据用于训练和验证.
主要成果:
- 机器学习模型成功地通过Pomeau-Manneville和危机引起的间歇性预测了过渡点到极端事件.
- 极端事件的吸引力和统计性质 (事件分布,事件间隔) 被准确地复制.
- 时间进化预测仅限于几次Lyapunov时间,但极端事件的幅度被保留或准确地预测.
结论:
- 储计算为预测复杂动态系统中的极端事件提供了一种有希望的方法.
- 该方法有效地捕捉了极端事件的动态和统计特征,特别是间歇性.
- 虽然短期时间演变预测是有限的,但振幅预测和过渡点预测显示出高准确度.
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