Engineering chimera and novel technique based on machine learning

Abstract

Recently, machine learning techniques have been put into practice in precisely characterizing various dynamical properties or phenomena. Here we make use of supervised machine learning algorithms for the model-free prediction of factors determining or controlling the intensity of symmetry breaking phenomena emergent in different network architectures. In an attempt to achieve this, chimera states (solitary states) are engineered by establishing delays in the neighboring links of a node (the interlayer links) in a 2-D lattice (multiplex network) of oscillators. Different machine learning classifiers, K-Nearest Neighbours (Knn), Support Vector Machine (SVM) and Multi-Layer Perceptron Neural Network (MLP-NN) are then employed, feeding on the data obtained from mentioned models, for the prediction of intensity of rippling chimera states and critical delay to characterize solitary states. It is revealed from our analysis that Multi-Layer Perceptron Neural Network (MLP-NN) classifier is best suited for the characterization of the engineered chimera and solitary states. We hope that our successful attempt in characterizing a class of partially synchronized states using machine learning techniques would be useful in broadening the scope of model-free machine learning techniques in characterizing other phase states as well.