Power grid networks, as well as neuronal networks with synaptic plasticity, describe real-world systems of tremendous importance for our daily life. The investigation of these seemingly unrelated types of dynamical networks has attracted increasing attention over the past decade. In this paper, we provide insight into the fundamental relation between these two types of networks. For this, we consider well-established models based on phase oscillators and show their intimate relation. In particular, we prove that phase oscillator models with inertia can be viewed as a particular class of adaptive networks. This relation holds even for more general classes of power grid models that include voltage dynamics. As an immediate consequence of this relation, we discover a plethora of multicluster states for phase oscillators with inertia. Moreover, the phenomenon of cascading line failure in power grids is translated into an adaptive neuronal network.

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自适应神经元网络教我们什么有关电网的知识

电网网络以及具有突触可塑性的神经网络描述了对我们的日常生活极为重要的现实世界系统。在过去的十年中，对这些看似无关的动力网络类型的研究引起了越来越多的关注。在本文中，我们提供了对这两种类型的网络之间的基本关系的见解。为此，我们考虑基于相位振荡器的完善模型，并显示它们的密切关系。特别是，我们证明具有惯性的相位振荡器模型可以看作是一类特殊的自适应网络。这种关系甚至适用于包括电压动态特性在内的更通用的电网模型类别。这种关系的直接后果是，我们发现了具有惯性的相位振荡器的过多的多簇状态。而且，