Zhang Han, Fan Huawei, Du Yao, Wang Liang, Wang Xingang
School of Physics and Information Technology, Shaanxi Normal University, Xi'an 710062, China.
School of Science, Xi'an University of Posts and Telecommunications, Xi'an 710121, China.
Chaos. 2022 Aug;32(8):083136. doi: 10.1063/5.0093663.
A model-free approach is proposed for anticipating the occurrence of measure synchronization in coupled Hamiltonian systems. Specifically, by the technique of parameter-aware reservoir computing in machine learning, we demonstrate that the machine trained by the time series of coupled Hamiltonian systems at a handful of coupling parameters is able to predict accurately not only the critical coupling for the occurrence of measure synchronization, but also the variation of the system order parameters around the transition point. The capability of the model-free technique in anticipating measure synchronization is exemplified in Hamiltonian systems of two coupled oscillators and also in a Hamiltonian system of three globally coupled oscillators where partial synchronization arises. The studies pave a way to the model-free, data-driven analysis of measure synchronization in large-size Hamiltonian systems.
提出了一种无模型方法来预测耦合哈密顿系统中测度同步的发生。具体而言，通过机器学习中的参数感知储层计算技术，我们证明，利用耦合哈密顿系统在少数耦合参数下的时间序列训练的机器，不仅能够准确预测测度同步发生时的临界耦合，还能预测系统序参量在转变点附近的变化。这种无模型技术预测测度同步的能力在两个耦合振子的哈密顿系统以及出现部分同步的三个全局耦合振子的哈密顿系统中得到了例证。这些研究为大尺寸哈密顿系统中测度同步的无模型、数据驱动分析铺平了道路。
