Eur. Phys. J. Spec. Top.
https://doi.org/10.1140/epjs/s11734-025-01548-5

Editorial

Data-based nonlinear and stochastic dynamics

¹ School of Mathematics and Statistics, Northwestern Polytechnical University, 710129, Xi’an, China
² Department of Physics, Humboldt University Berlin, 12489, Berlin, Germany
³ Department of Civil and Building Engineering, and Architecture, Polytechnic University of Marche, 60131, Ancona, Italy

^a hsux3@nwpu.edu.cn

Abstract

This special issue compiles 20 contributions, covering a wide range of latest achievements on dynamical modeling, data-driven algorithms, response predictions, multiple practical applications, and inverse problems. Data science plays a crucial role, helping us constructing more accurate dynamical models that capture and reflect the true dynamical changes of a system. At the same time, data science is also a powerful tool for deriving exact solutions of a system. By integrating it with deep learning algorithms, we are able to effectively predict the system responses and successfully apply this in several applications, such as airfoil flutter, arm musculoskeletal system, financial market, and epidemiology. In addition, inverse problems also occupy a pivotal position. Faced with the existing rich data, how to use data to identify the parameters of the model is still a challenging topic worthy of our continued attention and in-depth exploration.