通过庞加莱截面识别弹道模式
Identifying ballistic modes via Poincaré sections
AI总结：
本文提出基于图像处理的庞加莱截面分析方法，快速区分混沌与规则区域并分类输运状态，在标准映射中加速超扩散识别，并应用于双波含时哈密顿系统。
摘要：
通过庞加莱截面探索混沌系统在动力系统中已被证明至关重要，然而测量其特征以识别所考虑的各种动力学状态却面临挑战。本文提出了一种新方法，利用图像处理来区分保面积动力学中的混沌区域和规则区域，进而对输运状态进行分类。我们基于图像重建技术，用所提方法刻画了标准映射中的不同输运状态，识别超扩散的速度远快于通常的均方位移方法。该过程还应用于一个双波、含时哈密顿系统，以研究超扩散随两个参数的变化。
英文摘要：
Exploring chaotic systems via Poincaré sections has proven essential in dynamical systems, yet measuring their characteristics poses challenges to identify the various dynamical regimes considered. In this paper, we propose a new approach that uses image processing to distinguish chaotic and regular regions of area-preserving dynamics, and then classify the transport regime. We characterize different transport regimes in the standard map with the proposed method based on image reconstruction techniques, identifying the superdiffusion much faster than the usual mean square displacement method. The procedure is also applied to a two-wave, time-dependent Hamiltonian to investigate superdiffusion in function of two parameters.
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