Parallel Machine Learning for Forecasting the Dynamics of Complex Networks

Abstract

Forecasting the dynamics of large complex networks from previous time-series data is important in a wide range of contexts. Here we present a machine learning scheme for this task using a parallel architecture that mimics the topology of the network of interest. We demonstrate the utility and scalability of our method implemented using reservoir computing on a chaotic network of oscillators. Two levels of prior knowledge are considered: (i) the network links are known; and (ii) the network links are unknown and inferred via a data-driven approach to approximately optimize prediction.

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• Catch-22s of reservoir computing
• Reduced-order adaptive synchronization in a chaotic neural network with parameter mismatch: A dynamical system vs. machine learning approach
• Estimating the master stability function from the time series of one oscillator via reservoir computing
• Learning Beyond Experience: Generalizing to Unseen State Space with Reservoir Computing
• Phase transitions from linear to nonlinear information processing in neural networks