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Anticipating synchronization with machine learning
Physical Review Research
In realistic systems of coupled oscillators, it is desired to predict the onset of synchronization where the system equations are unknown, raising the need to develop a prediction framework that is model free and fully data driven. We show that this challenging problem can be addressed with machine learning. In particular, exploiting reservoir computing or echo state networks, we employ a "parameter-aware" scheme to train the neural machine using time series acquired from a small number ofdoi:10.1103/physrevresearch.3.023237 fatcat:omwnqepgmrg2fpuncgd5qau23u