无需参数先验，用自编码器从时序数据中自动提取驱动因子预测系统突变
Unsupervised learning for anticipating critical transitions
- 用变分自编码器无监督提取时序中的关键驱动因子
- 在库拉莫托-希瓦辛斯基系统上实现准确的临界突变预测
- 适用于多参数或部分观测场景，拓展性强
针对复杂动力系统中临界突变的预测问题，现有基于参数驱动的储层计算方法需预先知晓分岔参数。本文提出一种结合变分自编码器（VAE）与储层计算的框架：通过无监督学习从时序数据中检测驱动因子，并将提取信息作为参数输入储层计算机以预测临界转变。我们在典型的时空动力系统——库拉莫托-希瓦辛斯基系统上验证了该方法的有效性。该方案还可推广至多独立参数驱动或状态观测不完整的情形。
原文摘要 · Abstract (English)
For anticipating critical transitions in complex dynamical systems, the recent approach of parameter-driven reservoir computing requires explicit knowledge of the bifurcation parameter. We articulate a framework combining a variational autoencoder (VAE) and reservoir computing to address this challenge. In particular, the driving factor is detected from time series using the VAE in an unsupervised-learning fashion and the extracted information is then used as the parameter input to the reservoir computer for anticipating the critical transition. We demonstrate the power of the unsupervised learning scheme using prototypical dynamical systems including the spatiotemporal Kuramoto-Sivashinsky system. The scheme can also be extended to scenarios where the target system is driven by several independent parameters or with partial state observations.
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