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

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

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

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 …

A new smoother for a continuous dynamical state space model with sampled system
coefficients is proposed. This is completely different from conventional approaches, such as …

Linear multistep methods (LMMs) are popular time discretization schemes for solving the
forward problem on differential equations. Recently, LMMs together with deep neural …

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 …