Model reconstruction from temporal data for coupled oscillator networks
Model reconstruction from temporal data for coupled oscillator networks
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作者：
Panaggio, Mark J.;Ciocanel, Maria-Veronica;Xu, Bin
在复杂系统中，个体主体之间的相互作用经常导致自发同步、蜂群和模式形成等突发集体行为。除了代理的内在属性外，交互网络的拓扑结构可以对动力学产生巨大影响。在许多研究中，研究人员从每个智能体的内在动力学和相互作用网络的特定模型开始，并试图了解该模型的动力学。在这里，我们考虑反问题：给定来自系统的数据，人们能否了解模型和底层网络？我们研究了任意耦合的相位振荡器网络，它既可以表现出同步动力学，也可以表现出异步动力学。我们证明了，在给定关于每个振子瞬时演化的足够观测数据的情况下，机器学习可以重构相互作用网络并识别内在动力学。(C)2019年作者(S)。
In a complex system, the interactions between individual agents often lead to emergent collective behavior such as spontaneous synchronization, swarming, and pattern formation. Beyond the intrinsic properties of the agents, the topology of the network of interactions can have a dramatic influence over the dynamics. In many studies, researchers start with a specific model for both the intrinsic dynamics of each agent and the interaction network and attempt to learn about the dynamics of the model. Here, we consider the inverse problem: given data from a system, can one learn about the model and the underlying network? We investigate arbitrary networks of coupled phase oscillators that can exhibit both synchronous and asynchronous dynamics. We demonstrate that, given sufficient observational data on the transient evolution of each oscillator, machine learning can reconstruct the interaction network and identify the intrinsic dynamics. (C) 2019 Author(s).