Data-driven inference of complex system dynamics: A mini-review

¹ MOE Key Laboratory of Advanced Micro- Structured Materials and School of Physics Science and Engineering, Tongji University - Shanghai, PRC
² State Key Laboratory of Intelligent Autonomous Systems, Frontiers Science Center for Intelligent Autonomous Systems, and Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University - Shanghai, PRC
³ Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences - Shanghai, PRC

^(a) E-mail: eegyan@gmail.com (corresponding author)

Received: 18 December 2022
Accepted: 13 March 2023

Abstract

Our ability to observe the network topology and nodes' behaviors of complex systems has significantly advanced in the past decade, giving rise to a new and fast-developing frontier—inferring the underlying dynamical mechanisms of complex systems from the observation data. Here we explain the rationale of data-driven dynamics inference and review the recent progress in this emerging field. Specifically, we classify the existing methods of dynamics inference into three categories, and describe their key ideas, representative applications and limitations. We also discuss the remaining challenges that are worth the future effort.