Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics - Tapa blanda

 
9783039214099: Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics
Tapa blanda
ISBN 10: 3039214098 ISBN 13: 9783039214099
Editorial: MDPI AG, 2019

Sinopsis

The use of machine learning in mechanics is booming. Algorithms inspired by developments in the field of artificial intelligence today cover increasingly varied fields of application. This book illustrates recent results on coupling machine learning with computational mechanics, particularly for the construction of surrogate models or reduced order models. The articles contained in this compilation were presented at the EUROMECH Colloquium 597, « Reduced Order Modeling in Mechanics of Materials », held in Bad Herrenalb, Germany, from August 28th to August 31th 2018. In this book, Artificial Neural Networks are coupled to physics-based models. The tensor format of simulation data is exploited in surrogate models or for data pruning. Various reduced order models are proposed via machine learning strategies applied to simulation data. Since reduced order models have specific approximation errors, error estimators are also proposed in this book. The proposed numerical examples are very close to engineering problems. The reader would find this book to be a useful reference in identifying progress in machine learning and reduced order modeling for computational mechanics.

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Editorial
MDPI AG
Año de publicación
2019
Idioma
Inglés
ISBN 10 
3039214098
ISBN 13 
9783039214099
Encuadernación
Tapa blanda
Número de páginas
254
Editor
Fritzen Felix, Ryckelynck David
Contacto del fabricante
no disponible
Persona responsable
Buchpark GmbH
gpsr@buchpark.de

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