A non-intrusive proper generalized decomposition (PGD) strategy, coupled with an
overlapping domain decomposition (DD) method, is proposed to efficiently construct …

Reduced‐order models are essential tools to deal with parametric problems in the context of
optimization, uncertainty quantification, or control and inverse problems. The set of …

Reduced order models (ROM) are commonly employed to solve parametric problems and to
devise inexpensive response surfaces to evaluate quantities of interest in real-time. There …

A reduced order model technique is presented to perform the parametric Noise, Vibration
and Harshness (NVH) study of a vehicle body-in-white (BIW) structure characterized by …

This paper proposes a novel computational framework for the solution of geometrically
parametrised flow problems governed by the Stokes equation. The proposed method uses a …

Abstract The Inertia Relief (IR) technique is widely used by industry and produces
equilibrated loads allowing to analyze unconstrained systems without resorting to the more …

We propose in this paper a Proper Generalized Decomposition (PGD) solver for reduced-
order modeling of linear elastodynamic problems. It primarily focuses on enhancing the …

Uncertainty Quantification (UQ) is a key discipline for computational modeling of complex
systems, enhancing reliability of engineering simulations. In crashworthiness, having an …

An a priori reduced order method based on the proper generalised decomposition (PGD) is
proposed to compute parametric solutions involving turbulent incompressible flows of …

A nonintrusive reduced order method able to solve a parametric modal analysis is proposed
in this work. The main objective is being able to efficiently identify how a variation of user …