基于下一代储计算的第一阶段过渡的临界预测
Zhonghua Zhang1, Liang Wang1, Wei Xu1
1Northwestern Polytechnical University, School of Mathematics and Statistics, Xi'an 710072, China.
Physical review. E
|December 23, 2025
概括
这项研究引入了一个新的框架,用于预测复杂系统中的关键过渡. 参数感知下一代水库计算 (PNGRC) 框架准确识别相位过渡点,减少数据需求和培训时间.
科学领域:
- 复杂系统动力学 复杂系统动力学
- 非线性动力学和混沌理论
- 计算物理与工程 计算物理与工程
背景情况:
- 准确预测第一阶段过渡中的关键点对于早期预警系统和复杂系统中的风险管理至关重要.
- 现有的方法经常与参数变化的复杂性以及在hysteresis区域内识别可比结构而斗争.
研究的目的:
- 开发和评估一种新的参数感知下一代储计算 (PNGRC) 框架,用于准确预测第一阶段转换中的关键点.
- 通过高阶非线性特征向量来增强系统状态和参数信息的识别.
- 通过减少数据要求和培训时间,提高预测关键转换的效率.
主要方法:
- 构建一个参数感知下一代储计算 (PNGRC) 框架,将控制参数嵌入非线性向量自回归模型中.
- 生成高阶非线性特征向量,对系统状态和参数信息进行编码.
- 训练一个双向参数感知模型,以捕捉前进/后退轨迹,并在歇斯底里区域内识别可视化的结构和过渡路径.
主要成果:
- 该PNGRC框架准确地重建系统轨迹,并对未见的条件进行概括,有效地捕捉周期翻倍的分叉结构和歇斯底里现象.
- 在三种合振荡器系统中,在各种动态模式中识别一级关键点的能力.
- 与传统的参数意识库计算方法相比,数据需求和培训时间大幅减少.
结论:
- 该PNGRC框架提供了一个高效的,数据驱动的范式,用于预测复杂系统中的第一阶段过渡.
- 这种方法为在出现关键过渡的系统中提供了早期预警和风险管理的强大工具.
- 这项研究强调了先进的储计算技术在分析复杂的非线性动态方面的潜力.
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