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希格斯振荡器中的极端事件:动态研究和预测方法
Wasif Ahamed M1,2, Kavitha R1, Chithiika Ruby V3,4
1PG & Research Department of Physics, Nehru Memorial College (Autonomous), Affiliated to Bharathidasan University, Puthanampatti, Tiruchirappalli 621 007, India.
Chaos (Woodbury, N.Y.)
|February 3, 2025
概括
这项研究探讨了希格斯振荡器的混乱动态,通过间歇性和内部危机揭示了极端事件. 一个长期短期记忆神经网络被训练来预测这些不可预测的,大幅度的游览.
科学领域:
- 非线性动力学和混沌理论
- 理论物理 理论物理
- 计算神经科学是一种神经科学.
背景情况:
- 动态系统可以表现出不可预测的大幅度外观.
- 希格斯振荡器是由球体波振荡器衍生而来的,当投射到欧几里德平面上时,它呈现出复杂的动态.
- 了解这些动态对于各种科学领域至关重要.
研究的目的:
- 为了研究两叉现象和通往混乱的路径,在一个被抑制的,驱动的一维希格斯振荡器中.
- 识别和描述因间歇性和内部危机而产生的极端事件.
- 使用机器学习开发极端事件的预测模型.
主要方法:
- 在和外部强迫下分析希格斯振荡器动态.
- 识别分叉,包括对称性破坏,周期翻倍和间歇性危机.
- 应用概率分布分析来确认极端事件.
- 在时间序列数据上训练长期短期记忆 (LSTM) 神经网络,用于事件预测.
主要成果:
- 希格斯振荡器通过间歇性危机呈现了一条通往混乱的路线,因为驱动参数的增加.
- 极端事件通过内部危机和概率分布研究来确定和确认.
- 该LSTM模型展示了预测这些极端事件的能力.
结论:
- 希格斯振荡器是研究非线性系统中的混乱和极端事件的一个有价值的模型.
- 间歇性和内部危机是导致极端事件的关键机制.
- 在复杂的动态系统中,LSTM网络为预测极端事件提供了一个有前途的方法.
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