A review of machine learning methods applied to structural dynamics and vibroacoustic
Abstract The use of Machine Learning (ML) has rapidly spread across several fields of
applied sciences, having encountered many applications in Structural Dynamics and …
applied sciences, having encountered many applications in Structural Dynamics and …
A sparse Bayesian framework for discovering interpretable nonlinear stochastic dynamical systems with Gaussian white noise
Extracting governing physics from data is a key challenge in many areas of science and
technology. The existing techniques for equation discovery are mostly applicable to …
technology. The existing techniques for equation discovery are mostly applicable to …
On spike-and-slab priors for Bayesian equation discovery of nonlinear dynamical systems via sparse linear regression
This paper presents the use of spike-and-slab (SS) priors for discovering governing
differential equations of motion of nonlinear structural dynamic systems. The problem of …
differential equations of motion of nonlinear structural dynamic systems. The problem of …
Nonlinear dynamical system identification using the sparse regression and separable least squares methods
M Lin, C Cheng, Z Peng, X Dong, Y Qu… - Journal of Sound and …, 2021 - Elsevier
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 …
sparse regression algorithm and the separable least squares method. To effectively avoid …
[HTML][HTML] Sparse Bayesian machine learning for the interpretable identification of nonlinear structural dynamics: Towards the experimental data-driven discovery of a …
Data-driven discovery of governing laws for complex nonlinear structural dynamic systems
remains a challenging issue of paramount importance. This work addresses the above issue …
remains a challenging issue of paramount importance. This work addresses the above issue …
Discovering stochastic partial differential equations from limited data using variational Bayes inference
We propose a novel framework for discovering Stochastic Partial Differential Equations
(SPDEs) from data. The proposed approach combines the concepts of stochastic calculus …
(SPDEs) from data. The proposed approach combines the concepts of stochastic calculus …
A Bayesian framework for learning governing partial differential equation from data
The discovery of partial differential equations (PDEs) is a challenging task that involves both
theoretical and empirical methods. Machine learning approaches have been developed and …
theoretical and empirical methods. Machine learning approaches have been developed and …
Reconstruction of governing equations from vibration measurements for geometrically nonlinear systems
Data-driven system identification procedures have recently enabled the reconstruction of
governing differential equations from vibration signal recordings. In this contribution, the …
governing differential equations from vibration signal recordings. In this contribution, the …
Minimal model identification of drum brake squeal via SINDy
The industrial standard in the design and development process of NVH (Noise Vibration
Harshness) characteristic of brakes is the application of Finite Element (FE) models with a …
Harshness) characteristic of brakes is the application of Finite Element (FE) models with a …
Uncertainty analysis and experimental validation of identifying the governing equation of an oscillator using sparse regression
Y Ren, C Adams, T Melz - Applied Sciences, 2022 - mdpi.com
In recent years, the rapid growth of computing technology has enabled identifying
mathematical models for vibration systems using measurement data instead of domain …
mathematical models for vibration systems using measurement data instead of domain …