The recent development of machine learning (ML) and Deep Learning (DL) increases the
opportunities in all the sectors. ML is a significant tool that can be applied across many …

There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …

There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …

We develop a methodology to construct low-dimensional predictive models from data sets
representing essentially nonlinear (or non-linearizable) dynamical systems with a hyperbolic …

Abstract The use of Machine Learning (ML) has rapidly spread across several fields of
applied sciences, having encountered many applications in Structural Dynamics and …

Over the last 20 years Machine Learning (ML) has been applied to a wide variety of
applications in the fields of engineering and computer science. In the field of material …

This paper proposes a novel approach for learning a data-driven quadratic manifold from
high-dimensional data, then employing this quadratic manifold to derive efficient physics …

This contribution discusses surrogate models that emulate the solution field (s) in the entire
simulation domain. The surrogate uses the most characteristic modes of the solution field (s) …

ABSTRACT A primary spectral submanifold (SSM) is the unique smoothest nonlinear
continuation of a nonresonant spectral subspace E of a dynamical system linearized at a …

This work presents two novel approaches for the symplectic model reduction of high-
dimensional Hamiltonian systems using data-driven quadratic manifolds. Classical …