Physics-based computing exploits nonlinear or disorder-induced complexity, for example, to realize energy-efficient and high-throughput computing tasks. A particularly difficult but useful task is the prediction of extreme events that can occur in a wide range of complex systems. We prepare an experiment based on a microcavity semiconductor laser that produces statistically rare extreme events resulting from the interplay of deterministic nonlinear dynamics and spontaneous emission noise. We then evaluate the performance of three reservoir computing training approaches in predicting the occurrence of extreme events. We show that Dual Training Reservoir Computing (which in turn can be implemented with fast semiconductor laser dynamics) can provide meaningful early warnings up to 15 times the typical linear correlation time of the dynamics.

中文翻译：

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基于水库计算的极端事件预警

例如，基于物理的计算利用非线性或无序引起的复杂性来实现节能和高吞吐量的计算任务。一项特别困难但有用的任务是预测各种复杂系统中可能发生的极端事件。我们准备了一个基于微腔半导体激光器的实验，该激光器由于确定性非线性动力学和自发发射噪声的相互作用而产生统计上罕见的极端事件。然后，我们评估了三种水库计算训练方法在预测极端事件发生方面的性能。我们证明，双训练储层计算（又可以通过快速半导体激光动力学来实现）可以提供有意义的早期预警，其可达动力学典型线性相关时间的 15 倍。