- Hassan Ballout
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Cemosis, Institut de Recherche Mathématique Avancée, UMR 7501 Université de Strasbourg - CNRS email: [email protected]
- Yvon Maday
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Sorbonne Université, CNRS, Université Paris Cité, Laboratoire Jacques-Louis Lions (LJLL), F-75005 Paris, France email: [email protected] email: [email protected]
- Christophe Prud’homme
-
Cemosis, Institut de Recherche Mathématique Avancée, UMR 7501 Université de Strasbourg - CNRS email: [email protected]
Reduced basis methods for approximating the solutions of parameter-dependant partial differential equations (PDEs) are based on learning the structure of the set of solutions - seen as a manifold \(\mathcal S}\) in some functional space - when the parameters vary. This involves investigating the manifold and, in particular, understanding whether it is close to a low-dimensional affine space. This leads to the notion of Kolmogorov
If you use this template, please cite it as follows:
Christophe Prud'homme. “Feelpp/article.template: Release V1.4.0”. Zenodo, August 5, 2024. https://doi.org/10.5281/zenodo.13224368.
@software{christophe_prud_homme_2024_13224368,
author = {Christophe Prud'homme},
title = {feelpp/article.template: Release v1.4.0},
month = aug,
year = 2024,
publisher = {Zenodo},
version = {v1.4.0},
doi = {10.5281/zenodo.13224368},
url = {https://doi.org/10.5281/zenodo.13224368}
}