International audienceIn a posteriori error analysis of reduced basis approximations to affinely parametrized partial differential equations, the construction of lower bounds for the coercivity and inf-sup stability constants is essential. In [7], the authors presented an efficient method, compatible with an off-line/on-line strategy, where the on-line computation is reduced to minimizing a linear functional under a few linear constraints. These constraints depend on nested sets of parameters obtained iteratively using a greedy algorithm. We improve here this method so that it becomes more efficient due to a nice property, namely, that the computed lower bound is monotonically increasing with respect to the size of the nested sets. This imp...
We propose certified reduced basis methods for the efficient and reliable evaluation of a general ou...
For accurate a posteriori error analysis of the reduced basis method for coercive and non-coercive p...
Summarization: We present a technique for the rapid and reliable prediction of linear-functional out...
In a posteriori error analysis of reduced basis approximations to affinely parametrized partial dif...
In a posteriori error analysis of reduced basis approximations to affinely parametrized partial dif...
In a posteriori error analysis of reduced basis approximations to affinely parametrized partial dif...
In a posteriori error analysis of reduced basis approximations to affinely parametrized partial dif...
In a posteriori error analysis of reduced basis approximations to affinely parametrized partial diff...
We present a new approach for the construction of lower bounds for the inf-sup stability constants r...
We present a new approach for the construction of lower bounds for the inf-sup stability constants r...
We present a new approach for the construction of lower bounds for the inf-sup stability constants r...
We present a new approach for the construction of lower bounds for the inf-sup stability constants r...
Note presented by Olivier Pironneau. We present an approach to the construction of lower bounds for ...
Pour citer cet article: Y. Chen et al., C. R. Acad. Sci. Paris, Ser. I 346 (2008)International audie...
The Reduced Basis Method (RBM) generates low-order models of parametrized partial differential equat...
We propose certified reduced basis methods for the efficient and reliable evaluation of a general ou...
For accurate a posteriori error analysis of the reduced basis method for coercive and non-coercive p...
Summarization: We present a technique for the rapid and reliable prediction of linear-functional out...
In a posteriori error analysis of reduced basis approximations to affinely parametrized partial dif...
In a posteriori error analysis of reduced basis approximations to affinely parametrized partial dif...
In a posteriori error analysis of reduced basis approximations to affinely parametrized partial dif...
In a posteriori error analysis of reduced basis approximations to affinely parametrized partial dif...
In a posteriori error analysis of reduced basis approximations to affinely parametrized partial diff...
We present a new approach for the construction of lower bounds for the inf-sup stability constants r...
We present a new approach for the construction of lower bounds for the inf-sup stability constants r...
We present a new approach for the construction of lower bounds for the inf-sup stability constants r...
We present a new approach for the construction of lower bounds for the inf-sup stability constants r...
Note presented by Olivier Pironneau. We present an approach to the construction of lower bounds for ...
Pour citer cet article: Y. Chen et al., C. R. Acad. Sci. Paris, Ser. I 346 (2008)International audie...
The Reduced Basis Method (RBM) generates low-order models of parametrized partial differential equat...
We propose certified reduced basis methods for the efficient and reliable evaluation of a general ou...
For accurate a posteriori error analysis of the reduced basis method for coercive and non-coercive p...
Summarization: We present a technique for the rapid and reliable prediction of linear-functional out...