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使用数据驱动的主导平衡分析调制不稳定的相互作用动态和流波局部化
Andrei V Ermolaev1, Mehdi Mabed1, Christophe Finot2
1Université de Franche-Comté, Institut FEMTO-ST, CNRS UMR 6174, 25000, Besançon, France.
Scientific reports
|June 28, 2023
概括
机器学习可以自动识别驱动非线性系统中调制不稳定的物理过程. 这种数据驱动的主导平衡方法将非线性传播与分散驱动的本地化区分开来,即使是在混乱的场景中.
科学领域:
- 非线性光学是一种非线性光学.
- 数据驱动的科学数据驱动的科学
- 计算物理学的计算物理.
背景情况:
- 在光纤等非线性系统中的调制不稳定性 (MI) 对于理解波传播至关重要.
- 识别控制MI的主导物理过程通常依赖于专家直觉和异常分析.
- 现有的方法很难在混乱的动态中自动区分复杂的相互作用模式.
研究的目的:
- 开发和应用一个机器学习技术,数据驱动的主导平衡,用于自动分析MI动态.
- 在各种MI模式中区分非线性传播和非线性分散驱动的局部化.
- 将自动化分析扩展到复杂的,由噪声驱动的自发性MI和混乱传播.
主要方法:
- 应用数据驱动的主导平衡,一种机器学习方法,来分析非线性施罗丁格方程.
- 对阿赫梅迪耶夫呼吸器,库兹涅佐夫-马和佩雷格林单体 (流波) 结构的既定分析解决方案的解释.
- 对噪声驱动的自发调制不稳定性的数值模拟,以测试该方法的稳定性.
主要成果:
- 成功自动识别控制MI的主导物理过程.
- 主导非线性传播的区域与由联合非线性和分散驱动的区域区别.
- 在自发性MI和混乱的传播动态中,具有主导性的物理相互作用的孤立的独特模式.
结论:
- 数据驱动的主导平衡为分析复杂的非线性动态提供了一种有效的,自动化的方法.
- 该方法为在不同MI制度中区分物理驱动器提供了新的见解.
- 这种技术有望推动研究非线性系统中的波传播.
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