A system identification technique suitable for single degree of freedom (SDOF) and multiple
degree of freedom (MDOF) structural systems with either nonlinear elastic or …

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 …

This paper proposes a novel nonlinear dynamical system identification method based on the
sparse regression algorithm and the separable least squares method. To effectively avoid …

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 …

This paper presents a new Bayesian approach to equation discovery–combined structure
detection and parameter estimation–for system identification (SI) in nonlinear structural …

Adaptive estimation procedures have gained significant attention by the research community
to perform real-time identification of non-linear hysteretic structural systems under arbitrary …

The identification of structural damage is an important objective of health monitoring for civil
infrastructures. Frequently, damage to a structure may be reflected by a change of some …

This study presents a methodology for the on-line identification of nonlinear hysteretic
systems where not only the parameters of the system are unknown but also the nature of the …

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 …