论文标题
通过机器学习的有效模型和混乱多尺度系统的可预测性
Effective models and predictability of chaotic multiscale systems via machine learning
论文作者
论文摘要
我们仔细研究了基于储层计算的机器学习的使用，以构建数据驱动的多尺度混沌系统的有效模型。我们表明，对于大规模的分离，机器学习产生的有效模型类似于使用多尺度渐近技术获得的模型，并且在降低尺度分离时，也可以在可预测性方面有效。我们还表明，可以通过将储层与不完美模型杂交储层来提高可预测性。
We scrutinize the use of machine learning, based on reservoir computing, to build data-driven effective models of multiscale chaotic systems. We show that, for a wide scale separation, machine learning generates effective models akin to those obtained using multiscale asymptotic techniques and, remarkably, remains effective in predictability also when the scale separation is reduced. We also show that predictability can be improved by hybridizing the reservoir with an imperfect model.
