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 …

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 …

This work proposes a generalized methodology for sparse identification of dynamical
systems (SID), utilizing reweighted l 1-regularized least absolute deviation (LAD) regression …

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

This work develops a regularized least absolute deviation-based sparse identification of
dynamics (RLAD-SID) method to address outlier problems in the classical metric-based loss …

Dynamical systems play a fundamental role related understanding phenomena inherent to
several fields of science. Technological advances over the previous several decades have …

This paper proposes a non-convex penalty regression method to identify governing
equations of nonlinear dynamical systems from noisy state measurements. The idea to …

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

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

This paper studies the sparse identification problem of unknown sparse parameter vectors in
stochastic dynamic systems. Firstly, a novel sparse identification algorithm is proposed …