用深度学习从噪声中捕捉系统失稳前的闪烁信号。
Echoes Before Collapse: Deep Learning Detection of Flickering in Complex Systems
- 用卷积长短期记忆网络识别复杂系统的闪烁模式。
- 在真实数据上准确检测到地鼠体温和古气候记录中的闪烁。
- 适用于各类非线性动态系统，适合做早期预警研究。
深度学习为预测复杂系统临界点提供了强大工具，但其在检测闪烁（由噪声驱动的共存稳定态间切换）方面的潜力尚未探索。闪烁是气候系统、生态系统、金融市场等系统韧性下降的标志，可能预示重大但难以预测的相变。本文表明，基于简单多项式函数加噪声生成的合成时间序列训练的卷积长短期记忆（CNN LSTM）模型，能准确识别闪烁模式。尽管训练数据简化，模型仍可泛化至多种随机系统，并在真实数据中可靠检测到地鼠体温度记录及非洲湿润期古气候代理数据中的闪烁。结果表明，深度学习可从高噪声、非线性时间序列中提取早期预警信号，为广泛动力系统提供一种灵活的不稳定性检测框架。
原文摘要 · Abstract (English)
Deep learning offers powerful tools for anticipating tipping points in complex systems, yet its potential for detecting flickering (noise-driven switching between coexisting stable states) remains unexplored. Flickering is a hallmark of reduced resilience in climate systems, ecosystems, financial markets, and other systems. It can precede critical regime shifts that are highly impactful but difficult to predict. Here we show that convolutional long short-term memory (CNN LSTM) models, trained on synthetic time series generated from simple polynomial functions with additive noise, can accurately identify flickering patterns. Despite being trained on simplified dynamics, our models generalize to diverse stochastic systems and reliably detect flickering in empirical datasets, including dormouse body temperature records and palaeoclimate proxies from the African Humid Period. These findings demonstrate that deep learning can extract early warning signals from noisy, nonlinear time series, providing a flexible framework for identifying instability across a wide range of dynamical systems.
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