  In the study of network synchronization, an outstanding question of both
theoretical and practical significance is how to allocate a given set of
heterogenous oscillators on a complex network in order for improving the
synchronization performance. Whereas methods have been proposed to address this
question in literature, the methods are 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 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.
