标题： 无监督学习用于预测临界转换 显示英文标题

标题： Unsupervised learning for anticipating critical transitions

摘要： 为了预测复杂动力系统中的临界转变，最近的参数驱动储备计算方法需要明确了解分岔参数。 我们提出一个结合变分自编码器（VAE）和储备计算的框架来解决这一挑战。 特别是，通过无监督学习方式使用VAE从时间序列中检测驱动因素，然后将提取的信息作为参数输入到储备计算机中以预测临界转变。 我们使用包括时空Kuramoto-Sivashinsky系统的典型动力系统来展示无监督学习方案的强大功能。 该方案还可以扩展到目标系统由多个独立参数驱动或存在部分状态观测的情况。 显示英文摘要

摘要： 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.