arXiv 2201.028192022-01-08physics.optics

Kerr腔中混沌极端脉冲的前兆驱动机器学习预测

Precursor-driven machine learning prediction of chaotic extreme pulses in Kerr resonators

S. Coulibaly, F. Bessin, M. G. Clerc, A. Mussot

DOI：: 10.1016/j.chaos.2022.112199

摘要：

机器学习算法在高维混沌系统预测的堡垒中打开了一个缺口。他们发现数据中隐藏的相关性的能力可以被用来对时空混沌和极端事件进行无模型预测。然而，这些演变的广泛特征构成了全尺寸预测过程的关键限制。因此，预测相关事件的主要挑战是建立一套相关信息。在这里，我们从系统的传递熵和深度长短期记忆网络中识别出前兆，以预测在高维时空混沌状态下演化的系统的复杂动力学。从实验数据中测试了这种基于信息流图的无模型预测协议的性能，实验数据来自于在这种复杂的非线性状态下工作的无源谐振器。我们已经能够预测极端事件的发生，在检测到前兆之后，3倍于李雅普诺夫指数提供的地平线，92%的真阳性预测导致60%的准确度。

英文摘要：

Machine learning algorithms have opened a breach in the fortress of the prediction of high-dimensional chaotic systems. Their ability to find hidden correlations in data can be exploited to perform model-free forecasting of spatiotemporal chaos and extreme events. However, the extensive feature of these evolutions constitutes a critical limitation for full-size forecasting processes. Hence, the main challenge for forecasting relevant events is to establish the set of pertinent information. Here, we identify precursors from the transfer entropy of the system and a deep Long Short-Term Memory network to forecast the complex dynamics of a system evolving in a high-dimensional spatiotemporal chaotic regime. Performances of this triggerable model-free prediction protocol based on the information flowing map are tested from experimental data originating from a passive resonator operating in such a complex nonlinear regime. We have been able to predict the occurrence of extreme events up to 9 round trips after the detection of precursor, i.e., 3 times the horizon provided by Lyapunov exponents, with 92 % of true positive predictions leading to 60 % of accuracy.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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