Extreme events in globally coupled chaotic maps

Understanding and predicting uncertain things are the central themes of scientific evolution. Human beings revolve around these fears uncertainties concerning various aspects like a global pandemic, health, finances, to name but few. Dealing with this unavoidable part life is far tougher due chaotic nature unpredictable activities. In present article, we consider network identical maps, which splits into two different clusters, despite interaction between all nodes uniform. The stability analysis spatially homogeneous solutions provides critical coupling strength, before anticipate such partial synchronization. distance synchronized populations often deviates more than eight times standard deviation from its long-term average. probability density function highly deviated values fits well Generalized Extreme Value distribution. Meanwhile, distribution recurrence time intervals extreme events resembles Weibull existing literature helps us characterize as using significant height. These extremely high fluctuations less frequent in terms their occurrence. We determine numerically range strength for large recurrent events. On-off intermittency responsible mechanism underlying formation Besides understanding generation statistical signature, furnish forecasting powerful deep learning algorithms an artificial neural network. This Long Short-Term Memory (LSTM) can offer handy one-step intermittent bursts. also ensure robustness model hundred hidden cells each LSTM layer.