论文标题
前体驱动的机器学习预测Kerr谐振器中混沌极端脉冲的预测
Precursor-driven machine learning prediction of chaotic extreme pulses in Kerr resonators
论文作者
论文摘要
机器学习算法已经在预测高维混沌系统的堡垒中打开了违规行为。他们可以利用他们在数据中找到隐藏相关性的能力，以对时空混乱和极端事件进行无模型的预测。但是，这些发展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相关信息集。在这里，我们从系统的传输熵和深长的短期存储网络中确定前体，以预测系统在高维时空混乱状态下进化的系统的复杂动力学。基于信息流映射的该可触发的无模型预测协议的性能是从实验数据中测试的，该实验数据源自在这种复杂的非线性方案中运行的被动谐振器。在检测前体后，我们已经能够预测高达9个圆旅的极端事件的发生，即是Lyapunov指数提供的地平线的3倍，其中92％的真实积极预测导致准确性的60％。
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.
