On the verification of model reduction methods based on the proper generalized decomposition
By Pierre Ladevèze, Ludovic Chamoin. 2011
137 citations recorded by the source corpus.
Subfields: Computational Mechanics, Statistical and Nonlinear Physics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/e3374118ca0e684fbc4cf3842fdfa26a9abea87e
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mechanics
- Anthony Nouy
- Anthony T. Patera
- Yvon Maday
- Antonio Huerta
- Gianluigi Rozza
- Francisco Chinesta
- Amine Ammar
- Elías Cueto
In Statistical and Nonlinear Physics
- Anthony Nouy
- Anthony T. Patera
- Amine Ammar
- Francisco Chinesta
- Elías Cueto
- Grégory Legrain
- P. Cartraud
- Gianluigi Rozza
In Statistics and Probability
- Anthony Nouy
- Amine Ammar
- Francisco Chinesta
- Elías Cueto
- Antonio Huerta
- Grégory Legrain
- P. Cartraud
- Adrien Leygue
Citations in the corpus
- Tensor-based methods for numerical homogenization from high-resolution images (2013, methodology): “Error estimation methods have been first introduced in the context of PGD in [2, 29].”
- PGD-Based Computational Vademecum for Efficient Design, Optimization and Control (2013, background): “The enrichment is stopped as soon as the equation residual or any other error estimator based on a quantity of interest [8, 50] is small enough.”
- A parameterized‐background data‐weak approach to variational data assimilation: formulation, analysis, and application to acoustics (2015, methodology): “We may in addition consider other model order reduction approaches, such as the proper generalized decomposition [27].”