Active-learning-based nonintrusive model order reduction

Model order reduction (MOR) can provide low-dimensional numerical models for fast simulation. Unlike intrusive methods, nonintrusive methods are attractive because they can be applied even without access to full order models (FOMs). Since nonintrusive MOR methods strongly rely on snapshots of the FO...

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Autors principals:	Qinyu Zhuang, Dirk Hartmann, Hans-J. Bungartz, Juan M. Lorenzi
Format:	Artigo
Idioma:	Inglês
Publicat:	Cambridge University Press 2023-01-01
Col·lecció:	Data-Centric Engineering
Matèries:	Active learning joint space sampling machine learning model order reduction probably approximately correct learning
Accés en línia:	https://www.cambridge.org/core/product/identifier/S2632673622000399/type/journal_article
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