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 …

Recent advances in the field of data-driven dynamics allow for the discovery of ODE systems
using state measurements. One approach, known as Sparse Identification of Nonlinear …

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

Identifying governing equations from data is a critical step in the modeling and control of
complex dynamical systems. Here, we investigate the data-driven identification of nonlinear …

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 …

Automated data-driven modeling, the process of directly discovering the governing
equations of a system from data, is increasingly being used across the scientific community …

In many practical situations, it is highly desirable to estimate an accurate mathematical
model of a real system using as few parameters as possible. At the same time, the need for …

Big data have become a critically enabling component of emerging mathematical methods
aimed at the automated discovery of dynamical systems, where first principles modeling may …

This paper addresses the problem of identifying sparse linear time-invariant (LTI) systems
from a single sample trajectory generated by the system dynamics. We introduce a Lasso …