适应式储库计算:一种用于非线性动态系统中关键过渡的无模型数据驱动预测的范式
Shirin Panahi1, Ying-Cheng Lai1,2
1School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, Arizona 85287, USA.
Chaos (Woodbury, N.Y.)
|May 8, 2024
概括
在复杂的动态系统中预测关键转换是具有挑战性的. 适应式储库计算是一种机器学习方法,可以使用过渡前数据预测系统崩.
科学领域:
- 非线性动力学是一种非线性动力学.
- 复杂的系统复杂的系统.
- 机器学习 机器学习
背景情况:
- 在没有系统方程的情况下,很难预测动态系统中的关键过渡.
- 数据通常来自临界前状态,这使得系统崩的预测变得复杂.
研究的目的:
- 引入可适应的储库计算,用于预测关键过渡.
- 证明其在预测系统崩方面的有效性.
主要方法:
- 使用可适应的储计算,一种机器学习方法.
- 从前临界参数值使用时间序列来训练系统.
- 使模型能够学习与分叉参数的动态变化.
主要成果:
- 准确可靠地预测灾难性的关键过渡.
- 在预测危机,预测振幅死亡和创建数字双胞胎方面成功应用.
- 证明了学习系统动态如何随着分叉参数的变化的能力.
结论:
- 可适应的储计算为预测复杂系统中的关键过渡提供了一个强大的工具.
- 这种机器学习方法可以预测系统崩,即使具有有限的临界前数据.
- 该方法在理解和预测非线性动态行为方面具有广泛的应用.
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