This paper revisits a new model reduction methodology based on the use of separated
representations, the so called Proper Generalized Decomposition—PGD. Space and time …

This book provides a basic introduction to reduced basis (RB) methods for problems
involving the repeated solution of partial differential equations (PDEs) arising from …

Repeatedly solving nonlinear partial differential equations with varying parameters is often
an essential requirement to characterise the parametric dependences of dynamical systems …

Although being a popular approach for the modeling of laminated composites, mesoscale
constitutive models often struggle to represent material response for arbitrary load cases. A …

Abstract We demonstrate a Model Order Reduction technique for a system of nonlinear
equations arising from the Finite Element Method (FEM) discretization of the three …

This paper focuses on a new framework for obtaining a nonintrusive (ie, not requiring
projecting of the governing equations onto the reduced basis modes) reduced order model …

The need of the aerospace industry, at national or European level, of faster yet reliable
computational fluid dynamics models is the main drive for the application of model reduction …

We propose a non-intrusive stochastic model reduction method for polynomial chaos
representation of acoustic problems using proper orthogonal decomposition. The random …

Tensor methods are among the most prominent tools for the numerical solution of high-
dimensional problems where functions of multiple variables have to be approximated. These …

This paper presents a weak-intrusive stochastic finite element method for solving stochastic
structural dynamics equations. In this method, the stochastic solution is decomposed into the …