Artículos relacionados a Manifold Learning: Model Reduction in Engineering (SpringerB...
Manifold Learning: Model Reduction in Engineering (SpringerBriefs in Computer Science) - Tapa blanda
Sinopsis
This Open Access book reviews recent theoretical and numerical developments in nonlinear model order reduction in continuum mechanics, being addressed to Master and PhD students, as well as to researchers, lecturers and instructors. The aim of the authors is to provide tools for a better understanding and implement reduced order models by using: physics-based models, synthetic data forecast by these models, experimental data and deep learning algorithms. The book involves a survey of key methods of model order reduction applied to model-based engineering and digital twining, by learning linear or nonlinear latent spaces.
Projection-based reduced order models are the projection of mechanical equations on a latent space that have been learnt from both synthetic data and experimental data. Various descriptions and representations of structured data for model reduction are presented in the applications and survey chapters. Image-based digital twins are developed in a reduced setting. Reduced order models of as-manufactured components predict the mechanical effects of shape variations. A similar workflow is extended to multiphysics or coupled problems, with high dimensional input fields. Practical techniques are proposed for data augmentation and also for hyper-reduction, which is a key point to speed up projection-based model order reduction of finite element models.
The book gives access to python libraries available on gitlab.com, which have been developed as part of the research program [FUI-25] MORDICUS funded by the French government. Similarly to deep learning for computer vision, deep learning for model order reduction circumvents the need to design parametric problems prior reducing models. Such an approach is highly relevant for image-base modelling or multiphysics modelling.
"Sinopsis" puede pertenecer a otra edición de este libro.
Acerca del autor
David Ryckelynck is working on model-based/physics-based engineering assisted by machine learning. He did seminal works on hyper-reduction methods, in the field of applied mathematics and computational mechanics. He is the head of a lecture on Ingénierie Digitale Des Systemes Complexes (Data Science for Computational Engineering) at Mines Paris PSL University.
Fabien Casenave is a research scientist at Safran Tech, the research center of Safran Group, a French multinational company that designs, develops and manufactures aircraft engines, rocket engines as well as various aerospace and defense-related equipment or their components. As head of the Physics-Informed AI and Numerical Experiments team, Fabien has been working on model-based/physics-based engineering assisted by machine learning applied to industrial design challenges in structural mechanics.
Nissrine Akkari is a research scientist at Safran Tech. She has been working on model-based/physics-based engineering assisted by machine learning applied to industrial design challenges in computational fluid dynamics.
De la contraportada
"Sobre este título" puede pertenecer a otra edición de este libro.
EUR 20,07
EUR 17,21 gastos de envío desde Estados Unidos de America a España
Destinos, gastos y plazos de envíoComprar nuevo
Ver este artículo
EUR 19,19
EUR 17,21 gastos de envío desde Estados Unidos de America a España
Destinos, gastos y plazos de envío
Resultados de la búsqueda para Manifold Learning: Model Reduction in Engineering (SpringerB...
Imagen de archivo
Manifold Learning : Model Reduction in Engineering
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: 47352579-n
Cantidad disponible: Más de 20 disponibles
Imagen de archivo
Manifold Learning: Model Reduction in Engineering (SpringerBriefs in Computer Science)
Librería: Ria Christie Collections, Uxbridge, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Condición: New. In. Nº de ref. del artículo: ria9783031527661_new
Cantidad disponible: Más de 20 disponibles
Imagen de archivo
Manifold Learning : Model Reduction in Engineering
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: As New. Unread book in perfect condition. Nº de ref. del artículo: 47352579
Cantidad disponible: Más de 20 disponibles
Imagen de archivo
Manifold Learning: Model Reduction in Engineering (SpringerBriefs in Computer Science)
Librería: Zubal-Books, Since 1961, Cleveland, OH, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: New. 120 pp., paperback, NEW!! - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country. Nº de ref. del artículo: ZB1312326
Cantidad disponible: 1 disponibles
Imagen del vendedor
Manifold Learning
Publicado por
Springer, Berlin, Springer Nature Switzerland, Association Pour La Recherche Et Le Développement Des Méthodes Et Processus Industriels, Springer, 2024
ISBN 10: 3031527666
ISBN 13: 9783031527661
Nuevo
Taschenbuch
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 107 pp. Englisch. Nº de ref. del artículo: 9783031527661
Cantidad disponible: 2 disponibles
Imagen del vendedor
Manifold Learning : Model Reduction in Engineering
Publicado por
Springer Nature Switzerland, 2024
ISBN 10: 3031527666
ISBN 13: 9783031527661
Nuevo
Taschenbuch
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering. Nº de ref. del artículo: 9783031527661
Cantidad disponible: 1 disponibles
Imagen de archivo
Manifold Learning: Model Reduction in Engineering (SpringerBriefs in Computer Science)
Librería: California Books, Miami, FL, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: I-9783031527661
Cantidad disponible: Más de 20 disponibles
Imagen de archivo
Manifold Learning : Model Reduction in Engineering
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: 47352579-n
Cantidad disponible: Más de 20 disponibles
Imagen del vendedor
Manifold Learning
Publicado por
Springer Nature Switzerland, 2024
ISBN 10: 3031527666
ISBN 13: 9783031527661
Nuevo
Kartoniert / Broschiert
Librería: moluna, Greven, Alemania
Calificación del vendedor: 5 de 5 estrellas
Kartoniert / Broschiert. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Shows how manifold learning uses model order reduction and deep learning for training models in continuum mechanicsDiscusses high dimensional input variables in mechanical models, in particular for image-based digital twiningProposes practi. Nº de ref. del artículo: 1303460570
Cantidad disponible: Más de 20 disponibles
Imagen del vendedor
Manifold Learning
Publicado por
Springer Nature Switzerland, Springer International Publishing Feb 2024, 2024
ISBN 10: 3031527666
ISBN 13: 9783031527661
Nuevo
Taschenbuch
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. Neuware -This Open Access book reviews recent theoretical and numerical developments in nonlinear model order reduction in continuum mechanics, being addressed to Master and PhD students, as well as to researchers, lecturers and instructors. The aim of the authors is to provide tools for a better understanding and implement reduced order models by using: physics-based models, synthetic data forecast by these models, experimental data and deep learning algorithms. The book involves a survey of key methods of model order reduction applied to model-based engineering and digital twining, by learning linear or nonlinear latent spaces.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 120 pp. Englisch. Nº de ref. del artículo: 9783031527661
Cantidad disponible: 2 disponibles