We propose a comprehensive framework able to address both the predictability of the first and of the second kind for high-dimensional chaotic models. For this purpose, we analyse the properties of a newly introduced multistable climate toy model constructed by coupling the Lorenz ’96 model with a zero-dimensional energy balance model. First, the attractors of the system are identified with Monte Carlo Basin Bifurcation Analysis. Additionally, we are able to detect the Melancholia state separating the two attractors. Then, Neural Ordinary Differential Equations are applied to predict the future state of the system in both of the identified attractors.

中文翻译：

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用基于物理的机器学习方法分析双稳态气候玩具模型

我们提出了一个综合框架，能够解决高维混沌模型的第一类和第二类的可预测性。为此，我们分析了通过将 Lorenz '96 模型与零维能量平衡模型耦合而构建的新引入的多稳态气候玩具模型的特性。首先，系统的吸引子通过蒙特卡洛盆地分岔分析确定。此外，我们能够检测分离两个吸引子的忧郁状态。然后，应用神经常微分方程来预测系统在两个识别的吸引子中的未来状态。