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 …
[HTML][HTML] A review of inverse problems for generalized elastic media: formulations, experiments, synthesis
Starting from the seminal works of Toupin, Mindlin and Germain, a wide class of generalized
elastic models have been proposed via the principle of virtual work, by postulating …
elastic models have been proposed via the principle of virtual work, by postulating …
[HTML][HTML] Reduced order modeling of parametrized systems through autoencoders and SINDy approach: continuation of periodic solutions
Highly accurate simulations of complex phenomena governed by partial differential
equations (PDEs) typically require intrusive methods and entail expensive computational …
equations (PDEs) typically require intrusive methods and entail expensive computational …
[HTML][HTML] Deep learning methods for partial differential equations and related parameter identification problems
Recent years have witnessed a growth in mathematics for deep learning—which seeks a
deeper understanding of the concepts of deep learning with mathematics and explores how …
deeper understanding of the concepts of deep learning with mathematics and explores how …
[HTML][HTML] Deep learning-based reduced order models in cardiac electrophysiology
Predicting the electrical behavior of the heart, from the cellular scale to the tissue level, relies
on the numerical approximation of coupled nonlinear dynamical systems. These systems …
on the numerical approximation of coupled nonlinear dynamical systems. These systems …
[HTML][HTML] Thermodynamics-informed neural networks for physically realistic mixed reality
The imminent impact of immersive technologies in society urges for active research in real-
time and interactive physics simulation for virtual worlds to be realistic. In this context …
time and interactive physics simulation for virtual worlds to be realistic. In this context …
Local Lagrangian reduced-order modeling for the Rayleigh-Taylor instability by solution manifold decomposition
Abstract The Rayleigh-Taylor instability is a classical hydrodynamic instability of great
interest in various disciplines of science and engineering, including astrophysics …
interest in various disciplines of science and engineering, including astrophysics …
[HTML][HTML] Low-dimensional data-based surrogate model of a continuum-mechanical musculoskeletal system based on non-intrusive model order reduction
Over the last decades, computer modeling has evolved from a supporting tool for
engineering prototype design to an ubiquitous instrument in non-traditional fields such as …
engineering prototype design to an ubiquitous instrument in non-traditional fields such as …
Canonical and noncanonical Hamiltonian operator inference
A method for the nonintrusive and structure-preserving model reduction of canonical and
noncanonical Hamiltonian systems is presented. Based on the idea of operator inference …
noncanonical Hamiltonian systems is presented. Based on the idea of operator inference …
A super-real-time three-dimension computing method of digital twins in space nuclear power
Digital twins (DTs) have attracted widespread attention in academia and industry in recent
years. It can accurately reflect the physical world in real-time, enabling online monitoring …
years. It can accurately reflect the physical world in real-time, enabling online monitoring …