论文标题
机器学习预测关键过渡和系统崩溃
Machine learning prediction of critical transition and system collapse
论文作者
论文摘要
在不依赖模型的情况下预测由于参数漂移而导致的关键过渡是非线性动力学和应用领域中的一个杰出问题。一个密切相关的问题是预测系统是否已经存在，或者系统是否在其崩溃之前处于瞬态状态。我们通过利用储层计算来合并参数输入通道来开发一个基于机器学习的模型解决方案。我们证明，当机器通过混乱的吸引子（即在临界过渡之前）在正常功能状态进行训练时，可以准确预测过渡点。值得注意的是，对于参数贯穿临界点，具有输入参数通道的机器不仅能够预测系统处于瞬态状态，而且还可以预测最终崩溃之前的平均瞬态时间。
To predict a critical transition due to parameter drift without relying on model is an outstanding problem in nonlinear dynamics and applied fields. A closely related problem is to predict whether the system is already in or if the system will be in a transient state preceding its collapse. We develop a model free, machine learning based solution to both problems by exploiting reservoir computing to incorporate a parameter input channel. We demonstrate that, when the machine is trained in the normal functioning regime with a chaotic attractor (i.e., before the critical transition), the transition point can be predicted accurately. Remarkably, for a parameter drift through the critical point, the machine with the input parameter channel is able to predict not only that the system will be in a transient state, but also the average transient time before the final collapse.
