In recent years, the rapid growth of computing technology has enabled identifying
mathematical models for vibration systems using measurement data instead of domain …

Identifying dynamical system models from measurements is a central challenge in the
structural dynamics community. Nonlinear system identification, in particular, is a big …

This work proposes an iterative sparse-regularized regression method to recover governing
equations of nonlinear dynamical systems from noisy state measurements. The method is …

Abstract Machine learning methods have revolutionized studies in several areas of
knowledge, helping to understand and extract information from experimental data. Recently …

In the field of science and engineering, identifying the nonlinear dynamics of systems from
data is a significant yet challenging task. In practice, the collected data are often …

A crucial challenge encountered in diverse areas of engineering applications involves
speculating the governing equations based upon partial observations. On this basis, a …

Numerous researches have shown that there are three main challenges in data-driven
model identification methods: high-dimensional measurements, system complexity and …

In this paper, an evolutionary‐based sparse regression algorithm is proposed and applied
onto experimental data collected from a Duffing oscillator setup and numerical simulation …

Abstract The Sparse Identification of Nonlinear Dynamics (SINDy) toolbox can be used to
estimate a nonlinear model of dynamical systems. SINDy is a dictionary method that applies …

This paper proposes a joint sparse least-square model that utilizes a generalized fused
lasso penalty to jointly identify governing equations of nonlinear dynamical systems from …