Early Predictor for the Onset of Critical Transitions in Networked Dynamical Systems

IF 11.6 1区 物理与天体物理 Q1 PHYSICS, MULTIDISCIPLINARY Physical Review X Pub Date : 2024-07-15 DOI:10.1103/physrevx.14.031009
Zijia Liu, Xiaozhu Zhang, Xiaolei Ru, Ting-Ting Gao, Jack Murdoch Moore, Gang Yan
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Abstract

Numerous natural and human-made systems exhibit critical transitions whereby slow changes in environmental conditions spark abrupt shifts to a qualitatively distinct state. These shifts very often entail severe consequences; therefore, it is imperative to devise robust and informative approaches for anticipating the onset of critical transitions. Real-world complex systems can comprise hundreds or thousands of interacting entities, and implementing prevention or management strategies for critical transitions requires knowledge of the exact condition in which they will manifest. However, most research so far has focused on low-dimensional systems and small networks containing fewer than ten nodes or has not provided an estimate of the location where the transition will occur. We address these weaknesses by developing a deep-learning framework which can predict the specific location where critical transitions happen in networked systems with size up to hundreds of nodes. These predictions do not rely on the network topology, the edge weights, or the knowledge of system dynamics. We validate the effectiveness of our machine-learning-based framework by considering a diverse selection of systems representing both smooth (second-order) and explosive (first-order) transitions: the synchronization transition in coupled Kuramoto oscillators; the sharp decline in the resource biomass present in an ecosystem; and the abrupt collapse of a Wilson-Cowan neuronal system. We show that our method provides accurate predictions for the onset of critical transitions well in advance of their occurrences, is robust to noise and transient data, and relies only on observations of a small fraction of nodes. Finally, we demonstrate the applicability of our approach to real-world systems by considering empirical vegetated ecosystems in Africa.

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网络动力系统临界转换开始的早期预测器
许多自然和人造系统都会出现临界转换,环境条件的缓慢变化会引发突然的质变。这些转变往往会带来严重后果;因此,当务之急是设计出稳健且信息丰富的方法来预测临界转变的发生。现实世界中的复杂系统可能由成百上千个相互作用的实体组成,要实施临界转换的预防或管理策略,就必须了解临界转换的确切条件。然而,迄今为止,大多数研究都集中在低维系统和包含少于十个节点的小型网络上,或者没有提供对过渡发生位置的估计。为了解决这些问题,我们开发了一种深度学习框架,它可以预测在多达数百个节点的网络系统中发生关键转变的具体位置。这些预测不依赖于网络拓扑结构、边缘权重或系统动态知识。我们考虑了代表平滑(二阶)和爆炸(一阶)过渡的各种系统,验证了我们基于机器学习的框架的有效性:耦合仓本振荡器中的同步过渡;生态系统中资源生物量的急剧下降;以及威尔逊-考恩神经元系统的突然崩溃。我们的研究表明,我们的方法能在临界转换发生之前提前准确预测临界转换的发生,对噪声和瞬态数据具有鲁棒性,并且只依赖于对一小部分节点的观测。最后,我们通过考虑非洲的经验植被生态系统,证明了我们的方法在现实世界系统中的适用性。
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来源期刊
Physical Review X
Physical Review X PHYSICS, MULTIDISCIPLINARY-
CiteScore
24.60
自引率
1.60%
发文量
197
审稿时长
3 months
期刊介绍: Physical Review X (PRX) stands as an exclusively online, fully open-access journal, emphasizing innovation, quality, and enduring impact in the scientific content it disseminates. Devoted to showcasing a curated selection of papers from pure, applied, and interdisciplinary physics, PRX aims to feature work with the potential to shape current and future research while leaving a lasting and profound impact in their respective fields. Encompassing the entire spectrum of physics subject areas, PRX places a special focus on groundbreaking interdisciplinary research with broad-reaching influence.
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