We construct a reduced basis approximation for the solution to a system of nonlinear partial differential equations describing the temporal evolution of two populations following the Lotka-Volterra law. The first population’s carrying capacity contains a free parameter varying in a compact set. The reduced basis is constructed by two approaches: a proper orthogonal decomposition of a collection of solution snapshots and a greedy algorithm using an a posteriori error estimator.Bulgarian National Science Fund within the National Scientific Program Petar Beron i NIE of the Bulgarian Ministry of Education (contract number KP-06-DB-5)
AbstractCalibration of models is an important step in financial engineering. However it can be costl...
In this paper, a new efficient method called the parametric iteration method (PIM) is applied to acc...
In this thesis, we present a new reduced basis approach to parametrized saddle point problems. The p...
We construct a reduced basis approximation for the solution to a system of nonlinear partial differe...
Abstract. We address the task of model reduction of parametrized evolution equations. Detailed simul...
The Reduced Basis Method (RBM) is a model order reduction technique for solving parametric partial d...
The model order reduction methodology of reduced basis (RB) techniques offers efficient treatment of...
We address the task of model reduction for parametrized scalar hyperbolic or convection dominated pa...
Reduced order models, in particular the reduced basis method, rely on empirically built and problem ...
International audienceThe reduced basis method is a powerful model reduction technique designed to s...
During the last decades, reduced basis (RB) methods have been developed to a wide methodology for mo...
The convergence and efficiency of the reduced basis method used for the approximation of the solutio...
In this paper we extend the hierarchical model reduction framework based on reduced basis techniques...
With the Reduced Basis Method (RBM) we can solve a given parametrized PDE for many parameters which ...
In this article we introduce a new dimensional reduction approach which is based on the application ...
AbstractCalibration of models is an important step in financial engineering. However it can be costl...
In this paper, a new efficient method called the parametric iteration method (PIM) is applied to acc...
In this thesis, we present a new reduced basis approach to parametrized saddle point problems. The p...
We construct a reduced basis approximation for the solution to a system of nonlinear partial differe...
Abstract. We address the task of model reduction of parametrized evolution equations. Detailed simul...
The Reduced Basis Method (RBM) is a model order reduction technique for solving parametric partial d...
The model order reduction methodology of reduced basis (RB) techniques offers efficient treatment of...
We address the task of model reduction for parametrized scalar hyperbolic or convection dominated pa...
Reduced order models, in particular the reduced basis method, rely on empirically built and problem ...
International audienceThe reduced basis method is a powerful model reduction technique designed to s...
During the last decades, reduced basis (RB) methods have been developed to a wide methodology for mo...
The convergence and efficiency of the reduced basis method used for the approximation of the solutio...
In this paper we extend the hierarchical model reduction framework based on reduced basis techniques...
With the Reduced Basis Method (RBM) we can solve a given parametrized PDE for many parameters which ...
In this article we introduce a new dimensional reduction approach which is based on the application ...
AbstractCalibration of models is an important step in financial engineering. However it can be costl...
In this paper, a new efficient method called the parametric iteration method (PIM) is applied to acc...
In this thesis, we present a new reduced basis approach to parametrized saddle point problems. The p...