论文标题
一种数据驱动的方法，用于发现随机碳循环系统最可能的过渡途径
A Data-Driven Approach for Discovering the Most Probable Transition Pathway for a Stochastic Carbon Cycle System
论文作者
论文摘要
许多自然系统都表现出尖端，使环境状况的变化突然转移到了新的，有时是完全不同的状态。全球气候变化通常与海洋碳储备的稳定性有关。我们考虑上海洋的随机碳酸盐系统，以捕获这种过渡现象。基于Onsager-Machlup动作功能理论，我们通过神经射击方法计算了亚稳态和振荡状态之间最可能的过渡途径，并进一步探索了外部随机碳输入速率对最可能的过渡途径的影响，这提供了一种基础，从而提供了识别自然倾斜点的基础。特别是，我们研究了过渡时间对过渡途径的影响，并使用物理学知情的神经网络进一步计算了最佳的过渡时间，并朝着振荡制度中的最大碳酸盐浓度状态。这项工作为简单模型中随机碳输入对气候过渡的影响提供了一些见解。关键词：Onsager-Machlup动作功能，最可能的过渡途径，神经射击方法，随机碳循环系统。
Many natural systems exhibit tipping points where changing environmental conditions spark a sudden shift to a new and sometimes quite different state. Global climate change is often associated with the stability of marine carbon stocks. We consider a stochastic carbonate system of the upper ocean to capture such transition phenomena. Based on the Onsager-Machlup action functional theory, we calculate the most probable transition pathway between the metastable and oscillatory states via a neural shooting method, and further explore the effects of external random carbon input rates on the most probable transition pathway, which provides a basis to recognize naturally occurring tipping points. Particularly, we investigate the effect of the transition time on the transition pathway and further compute the optimal transition time using physics informed neural network, towards the maximum carbonate concentration state in the oscillatory regimes. This work offers some insights on the effects of random carbon input on climate transition in a simple model. Key words: Onsager-Machlup action functional, the most probable transition pathway, neural shooting method, stochastic carbon cycle system.
