Special volume in honor of Professor David GottliebInternational audienceABSTRACT The convergence and efficiency of the reduced basis method used for the approximation of the solutions to a class of problems written as a parametrized PDE depends heavily on the choice of the elements that constitute the "reduced basis". The purpose of this paper is to analyze the a priori convergence for one of the approaches used for the selection of these elements, the greedy algorithm. Under natural hypothesis on the set of all solutions to the problem obtained when the parameter varies, we prove that three greedy algorithms converge; the last algorithm, based on the use of an a posteriori estimator, is the approach actually employed in the calculations
We consider the reduced basis generation in the offline stage. As an alternative for standard Greedy...
Abstract. This paper is a survey which also contains some new results on the nonlinear approximation...
In this paper, we extend the reduced-basis methods and associated a posteriori error estimators dev...
Special volume in honor of Professor David GottliebInternational audienceABSTRACT The convergence an...
The convergence and efficiency of the reduced basis method used for the approximation of the solutio...
The consecutive numbering of the publications is determined by their chronological order. The aim of...
Reduced bases have been introduced for the approximation of parametrized PDEs in applications where ...
Abstract: The reduced basis (RB) method is an efficient technique to solve parametric partial differ...
Iterative approximation algorithms are successfully applied in parametric approximation tasks. In pa...
The main theme of this volume is the efficient solution of families of stochastic or parametric part...
In the present paper the authors study second-order elliptic parametric partial differential equatio...
In the present paper the authors study second-order elliptic parametric partial differential equatio...
In this paper we present a compact review on the mostly used techniques for computational reduction ...
In this paper we present a compact review on the mostly used techniques for computational reduction ...
This book provides a basic introduction to reduced basis (RB) methods for problems involving the rep...
We consider the reduced basis generation in the offline stage. As an alternative for standard Greedy...
Abstract. This paper is a survey which also contains some new results on the nonlinear approximation...
In this paper, we extend the reduced-basis methods and associated a posteriori error estimators dev...
Special volume in honor of Professor David GottliebInternational audienceABSTRACT The convergence an...
The convergence and efficiency of the reduced basis method used for the approximation of the solutio...
The consecutive numbering of the publications is determined by their chronological order. The aim of...
Reduced bases have been introduced for the approximation of parametrized PDEs in applications where ...
Abstract: The reduced basis (RB) method is an efficient technique to solve parametric partial differ...
Iterative approximation algorithms are successfully applied in parametric approximation tasks. In pa...
The main theme of this volume is the efficient solution of families of stochastic or parametric part...
In the present paper the authors study second-order elliptic parametric partial differential equatio...
In the present paper the authors study second-order elliptic parametric partial differential equatio...
In this paper we present a compact review on the mostly used techniques for computational reduction ...
In this paper we present a compact review on the mostly used techniques for computational reduction ...
This book provides a basic introduction to reduced basis (RB) methods for problems involving the rep...
We consider the reduced basis generation in the offline stage. As an alternative for standard Greedy...
Abstract. This paper is a survey which also contains some new results on the nonlinear approximation...
In this paper, we extend the reduced-basis methods and associated a posteriori error estimators dev...