Local manifold learning and its link to domain-based physics knowledge

In many reacting flow systems, the thermo-chemical state-space is known or assumed to evolve close to a low-dimensional manifold (LDM). Various approaches are available to obtain those manifolds and subsequently express the original high-dimensional space with fewer parameterizing variables. Princip...

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Bibliographic Details
Main Authors:	Kamila Zdybał, Giuseppe D’Alessio, Antonio Attili, Axel Coussement, James C. Sutherland, Alessandro Parente
Format:	Article
Language:	English
Published:	Elsevier 2023-06-01
Series:	Applications in Energy and Combustion Science
Subjects:	Local principal component analysis Low-dimensional manifolds Physically interpretable models Data parameterization Data clustering Semi-supervised learning
Online Access:	http://www.sciencedirect.com/science/article/pii/S2666352X23000201

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http://www.sciencedirect.com/science/article/pii/S2666352X23000201

Local manifold learning and its link to domain-based physics knowledge

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