Identifying dynamical system models from measurements is a central challenge in the
structural dynamics community. Nonlinear system identification, in particular, is a big …

Data-driven modeling can help improve understanding of the governing equations for
systems that are challenging to model. In the current work, the Sparse Identification of …

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

Abstract Nonlinear Normal Modes (NNMs) have proven useful in a few recent works as a
basis for comparing nonlinear models during model updating. In prior works the authors …

Abstract Machine learning methods have revolutionized studies in several areas of
knowledge, helping to understand and extract information from experimental data. Recently …

Abstract Nonlinear Normal Modes (NNMs) offer tremendous insight into the dynamic
behavior of a nonlinear system, extending many concepts that are familiar in linear modal …

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

The nonlinear modes of a non-conservative nonlinear system are sometimes referred to as
damped nonlinear normal modes (dNNMs). Because of the non-conservative …

Presented here is a new time–frequency signal processing methodology based on Hilbert–
Huang transform (HHT) and a new conjugate-pair decomposition (CPD) method for …

In the present article, we follow up our recent work on the experimental assessment of two
data-driven nonlinear system identification methodologies. The first methodology constructs …