International audienceThe Proper Generalized Decomposition (PGD) is a methodology initially proposed for the solution of partial dierential equations (PDE) dened in tensor product spaces. It consists in constructing a separated representation of the solution of a given PDE. In this paper we consider the mathematical analysis of this framework for a larger class of problems in an abstract setting. In particular, we introduce a generalization of Eckart and Young theorem which allows to prove the convergence of the so-called progressive PGD for a large class of linear problems dened in tensor product Hilbert spaces
International audienceTensor-based methods are receiving a growing interest in scientific computing ...
Proper Generalized Decomposition (PGD) is devised as a computational method to solve high-dimensiona...
the date of receipt and acceptance should be inserted later Abstract Tensor-based methods are receiv...
AbstractThe Proper Generalized Decomposition (PGD) is a methodology initially proposed for the solut...
Agraïments: A. Nouy supported by GdR MoMaS with partners ANDRA, BRGM, CEA, CNRS, EDF, IRSNThe Proper...
International audienceModel reduction techniques based on the construction of separated representati...
We introduce a new algorithm of proper generalized decomposition (PGD) for parametric symmetric elli...
The Proper Generalized Decomposition or, for short, PGD is a tensor decomposition based technique to...
A Proper Generalized Decomposition for the solution of elliptic problems in abstract form by using a...
International audienceOver the past years, model reduction techniques have become a necessary path f...
International audienceWe introduce a new algorithm of proper generalized decomposition (PGD) for par...
In this study, we present the mathematical analysis needed to explain the convergence of a progressi...
In its original conception, proper generalized decomposition (PGD) provides explicit parametric solu...
In this thesis, we are interested in the PGD (Proper Generalized Decomposition), one of the reduced ...
In this thesis a recently proposed method for the efficient approximation of solutions to high-dime...
International audienceTensor-based methods are receiving a growing interest in scientific computing ...
Proper Generalized Decomposition (PGD) is devised as a computational method to solve high-dimensiona...
the date of receipt and acceptance should be inserted later Abstract Tensor-based methods are receiv...
AbstractThe Proper Generalized Decomposition (PGD) is a methodology initially proposed for the solut...
Agraïments: A. Nouy supported by GdR MoMaS with partners ANDRA, BRGM, CEA, CNRS, EDF, IRSNThe Proper...
International audienceModel reduction techniques based on the construction of separated representati...
We introduce a new algorithm of proper generalized decomposition (PGD) for parametric symmetric elli...
The Proper Generalized Decomposition or, for short, PGD is a tensor decomposition based technique to...
A Proper Generalized Decomposition for the solution of elliptic problems in abstract form by using a...
International audienceOver the past years, model reduction techniques have become a necessary path f...
International audienceWe introduce a new algorithm of proper generalized decomposition (PGD) for par...
In this study, we present the mathematical analysis needed to explain the convergence of a progressi...
In its original conception, proper generalized decomposition (PGD) provides explicit parametric solu...
In this thesis, we are interested in the PGD (Proper Generalized Decomposition), one of the reduced ...
In this thesis a recently proposed method for the efficient approximation of solutions to high-dime...
International audienceTensor-based methods are receiving a growing interest in scientific computing ...
Proper Generalized Decomposition (PGD) is devised as a computational method to solve high-dimensiona...
the date of receipt and acceptance should be inserted later Abstract Tensor-based methods are receiv...