Speaker
Description
Networks of coupled dynamical systems are fundamental models across the sciences, from physics to neuroscience. Despite their success, the governing equations of such systems are often unknown, limiting our ability to predict and control their dynamics. In many applications, only time series data from the network is accessible, and learning the governing equations from data becomes an inverse problem. In this talk, inspired by compressive sensing techniques, I will show how learning network dynamics from data can be formulated as a convex optimization problem. By exploiting structural information encoded in the network dynamics, such as sparsity, statistical properties, and symmetries, we characterize the minimum amount of data required for learning the network dynamics exactly (and robustly). We illustrate these ideas using networks of coupled chaotic maps and oscillators.