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

In recent years, identification of nonlinear dynamical systems from data has become
increasingly popular. Sparse regression approaches, such as sparse identification of …

Accurately modelling the nonlinear dynamics of a system from measurement data is a
challenging yet vital topic. The sparse identification of nonlinear dynamics (SINDy) algorithm …

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

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

Abstract Sparse Identification of Nonlinear Dynamics (SINDy) has been shown to
successfully recover governing equations from data; however, this approach assumes the …

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

We develop an algorithm for model selection which allows for the consideration of a
combinatorially large number of candidate models governing a dynamical system. The …

Modern societies have an abundance of data yet good system models are rare.
Unfortunately, many of the current system identification and machine learning techniques fail …

This paper presents a generalized approach to identify the structure of governing nonlinear
equations of motion from the time history of state variables and control functions. An integral …