Tipping Point Detection Using Reservoir Computing

Detection in high fidelity of tipping points,the emergence of which is often induced by invisible changes in internal structures or/and external interferences,is paramountly beneficial to understanding and predicting complex dynamical systems(CDSs).Detection approaches,which have been fruitfully developed from several perspectives(e.g.,statistics,dynamics,and machine learning),have their own advantages but still encounter difficulties in the face of high-dimensional,fluctuating datasets.Here,using the reservoir computing(RC),a recently notable,resource-conserving machine learning method for reconstructing and predicting CDSs,we articulate a model-free framework to accomplish the detection only using the time series observationally recorded from the underlying unknown CDSs.