论文标题
用基于物理的机器学习方法分析可为气候玩具模型的分析
Analysis of a bistable climate toy model with physics-based machine learning methods
论文作者
论文摘要
我们提出了一个综合框架，能够解决高维混沌模型的第一和第二类的可预测性。为此，我们分析了通过将洛伦兹'96模型与零维能量平衡模型耦合而构建的新引入的多稳定气候玩具模型的性能。首先，通过蒙特卡洛盆地分叉分析来鉴定系统的吸引子。此外，我们能够检测到将两个吸引子分开的忧郁症状态。然后，应用神经普通微分方程，以预测两个已确定的吸引子中系统的未来状态。
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 in order to predict the future state of the system in both of the identified attractors.
