Data-driven discovery and extrapolation of parameterized pattern-forming dynamics

Abstract

A new machine-learning framework enables the discovery of governing equations in pattern-forming systems parameterized by external driving conditions. The resulting data-driven models reveal effective nonlinear corrections to classical perturbation theory, enabling extrapolation including the prediction of bifurcations far from the conditions used in training.