Inferring synchronizability of networked heterogeneous oscillators with machine learning

In the study of network synchronization, an outstanding question of both theoretical and practical significance is how to allocate a given set of heterogeneous oscillators on a complex network in order to improve the synchronization performance. Whereas methods have been proposed to address this question in the literature, the methods are all based on accurate models describing the system dynamics, which, however, are normally unavailable in realistic situations. Here, we show that this question can be addressed by the model-free technique of a feed-forward neural network (FNN) in machine learning. Specifically, we measure the synchronization performance of a number of allocation schemes and use the measured data to train a machine. It is found that the trained machine is able to not only infer the synchronization performance of any new allocation scheme, but also find from a huge amount of candidates the optimal allocation scheme for synchronization.

Automated summary

In the study of network synchronization, the question of how to allocate heterogeneous oscillators on a complex network for improved synchronization performance is addressed using a model-free technique of a feed-forward neural network. The measured synchronization performance data is used to train a machine that can predict the performance of new allocation schemes and find the optimal allocation scheme for synchronization from a large number of candidates.