International audienceScalability of parallel solvers for problems with high heterogeneities relies on adaptive coarse spaces built from generalized eigenvalue problems in the subdomains. The corresponding theory is powerful and flexible but the development of an efficient parallel implementation is challenging. We report here on recent advances in adaptive coarse spaces and on their open source implementations
In science and engineering, many problems exhibit multiscale properties, making the development of e...
In this paper, we discuss strategies for computing subsets of eigenvectors of matrices corresponding...
We present an analysis of the additive average Schwarz preconditioner with two newly proposed adapti...
International audienceScalability of parallel solvers for problems with high heterogeneities relies ...
International audienceScalability of parallel solvers for problems with high heterogeneities relies ...
The convergence rate of domain decomposition methods is generally determined by the eigenvalues of t...
A parallel FETI-DP domain decomposition method using an adaptive coarse space is presented. The impl...
The convergence rate of domain decomposition methods is generally determined by the eigenvalues of t...
We have developed a parallel adaptive eigenvalue solver and applied it to a model problem in theoret...
We propose to step away from the black-box approach and allow the eigensolver to accept a...
A robust two-level overlapping Schwarz method for scalar elliptic model problems with highly varying...
textThis thesis demonstrates an efficient parallel method of solving the generalized eigenvalue prob...
Structured adaptive mesh algorithms dynamically allocate computational resources to accurately resol...
We compare the spectra of local generalized eigenvalue problems in different adaptive coarse spaces ...
Two-level domain decomposition preconditioners lead to fast convergence and scalability of iterative...
In science and engineering, many problems exhibit multiscale properties, making the development of e...
In this paper, we discuss strategies for computing subsets of eigenvectors of matrices corresponding...
We present an analysis of the additive average Schwarz preconditioner with two newly proposed adapti...
International audienceScalability of parallel solvers for problems with high heterogeneities relies ...
International audienceScalability of parallel solvers for problems with high heterogeneities relies ...
The convergence rate of domain decomposition methods is generally determined by the eigenvalues of t...
A parallel FETI-DP domain decomposition method using an adaptive coarse space is presented. The impl...
The convergence rate of domain decomposition methods is generally determined by the eigenvalues of t...
We have developed a parallel adaptive eigenvalue solver and applied it to a model problem in theoret...
We propose to step away from the black-box approach and allow the eigensolver to accept a...
A robust two-level overlapping Schwarz method for scalar elliptic model problems with highly varying...
textThis thesis demonstrates an efficient parallel method of solving the generalized eigenvalue prob...
Structured adaptive mesh algorithms dynamically allocate computational resources to accurately resol...
We compare the spectra of local generalized eigenvalue problems in different adaptive coarse spaces ...
Two-level domain decomposition preconditioners lead to fast convergence and scalability of iterative...
In science and engineering, many problems exhibit multiscale properties, making the development of e...
In this paper, we discuss strategies for computing subsets of eigenvectors of matrices corresponding...
We present an analysis of the additive average Schwarz preconditioner with two newly proposed adapti...