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 …

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 …

Identification of nonlinear dynamical systems has been popularized by sparse identification
of the nonlinear dynamics (SINDy) via the sequentially thresholded least squares (STLS) …

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

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

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

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

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

This work proposes a method called fractional function group fractional function penalty
sparse least squares to identify nonlinear dynamical systems. It integrates least squares with …

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 …