A short review on model order reduction based on proper generalized decomposition

F Chinesta, P Ladeveze, E Cueto - Archives of Computational Methods in …, 2011‏ - Springer
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 …

Recent advances and new challenges in the use of the proper generalized decomposition for solving multidimensional models

F Chinesta, A Ammar, E Cueto - Archives of Computational methods in …, 2010‏ - Springer
This paper revisits a powerful discretization technique, the Proper Generalized
Decomposition—PGD, illustrating its ability for solving highly multidimensional models. This …

PGD-Based Computational Vademecum for Efficient Design, Optimization and Control

F Chinesta, A Leygue, F Bordeu, JV Aguado… - … methods in Engineering, 2013‏ - Springer
In this paper we are addressing a new paradigm in the field of simulation-based engineering
sciences (SBES) to face the challenges posed by current ICT technologies. Despite the …

Data-driven non-linear elasticity: constitutive manifold construction and problem discretization

R Ibañez, D Borzacchiello, JV Aguado… - Computational …, 2017‏ - Springer
The use of constitutive equations calibrated from data has been implemented into standard
numerical solvers for successfully addressing a variety problems encountered in simulation …

An overview of the proper generalized decomposition with applications in computational rheology

F Chinesta, A Ammar, A Leygue, R Keunings - Journal of Non-Newtonian …, 2011‏ - Elsevier
We review the foundations and applications of the proper generalized decomposition (PGD),
a powerful model reduction technique that computes a priori by means of successive …

Advanced simulation of models defined in plate geometries: 3D solutions with 2D computational complexity

B Bognet, F Bordeu, F Chinesta, A Leygue… - Computer Methods in …, 2012‏ - Elsevier
Many models in polymer processing and composites manufacturing are defined in
degenerated three-dimensional domains, involving plate or shell geometries. The reduction …

A multidimensional data‐driven sparse identification technique: the sparse proper generalized decomposition

R Ibáñez, E Abisset-Chavanne, A Ammar… - …, 2018‏ - Wiley Online Library
Sparse model identification by means of data is especially cumbersome if the sought
dynamics live in a high dimensional space. This usually involves the need for large amount …

Fast alternating LS algorithms for high order CANDECOMP/PARAFAC tensor factorizations

AH Phan, P Tichavský… - IEEE Transactions on …, 2013‏ - ieeexplore.ieee.org
CANDECOMP/PARAFAC (CP) has found numerous applications in wide variety of areas
such as in chemometrics, telecommunication, data mining, neuroscience, separated …

Proper generalized decompositions and separated representations for the numerical solution of high dimensional stochastic problems

A Nouy - Archives of Computational Methods in Engineering, 2010‏ - Springer
Uncertainty quantification and propagation in physical systems appear as a critical path for
the improvement of the prediction of their response. Galerkin-type spectral stochastic …

[PDF][PDF] Model order reduction

F Chinesta, A Huerta, G Rozza… - Encyclopedia of …, 2016‏ - ww2.lacan.upc.edu
This chapter presents an overview of Model Order Reduction–a new paradigm in the field of
simulationbased engineering sciences, and one that can tackle the challenges and leverage …