Revealing dynamics of non-autonomous complex systems from data

TL;DR

This paper presents a data-driven method for inferring non-autonomous dynamical equations, enabling the understanding and prediction of complex systems across various scientific and engineering fields.

Contribution

It introduces a versatile approach that identifies optimal basis functions for reconstructing non-autonomous system behavior from data, applicable to diverse real-world systems.

Findings

Successfully inferred governing equations for synthetic systems like cusp bifurcation and Kuramoto oscillators.

Extended the method to empirical systems such as cellular energy, UAV navigation, and marine fish communities.

Achieved accurate predictions and uncovered underlying laws of complex real-world systems.

Abstract

Discovering governing equations from data is crucial for understanding complex systems in many diverse fields from science to engineering. Yet, there still is a lack of versatile computational toolbox to deal with this long standing challenge due to the inherent non-autonomicity and unknowability of the underlying dynamics. Here, we introduce a data-driven approach for inferring non-autonomous dynamical equations by identifying an optimal set of basis functions within the model space, enabling the reconstruction of complex systems behavior under simplified prior specifications. Our method demonstrates effectiveness in equation discovery on canonical synthetic systems such as cusp bifurcation and coupled Kuramoto oscillators. Furthermore, we extend the application of this approach to leaf cellular energy, unmanned aerial vehicle navigation, chick-heart aggregates, and marine fish community under simple basis function libraries. Leveraging the inferred equations, we accurately predict the evolution of these empirical systems and further uncover their governing laws. Our approach offers a novel paradigm to reveal the underlying dynamics of a wide range of real-world systems.