Articles liés à Machine Learning in Modeling and Simulation: Methods...
- ÉditeurSpringer International Publishing AG
- Date d'édition2023
- ISBN 10 3031366433
- ISBN 13 9783031366437
- ReliureRelié
- Numéro d'édition1
- Nombre de pages451
- ÉditeurRabczuk Timon, Bathe Klaus-Jürgen
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Machine Learning in Modeling and Simulation
Edité par
Springer International Publishing, 2023
ISBN 10 : 3031366433
ISBN 13 : 9783031366437
Neuf
Couverture rigide
Vendeur : moluna, Greven, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Comprehensive state-of-the-art book on scientific machine learning approaches in modelling & simulationCovers the wide range of PDEs, uncertainty, optimization, inverse analysis, constitutive modelling & material designFocuses on engineerin. N° de réf. du vendeur 881233602
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Machine Learning in Modeling and Simulation
Edité par
Springer International Publishing Nov 2023, 2023
ISBN 10 : 3031366433
ISBN 13 : 9783031366437
Neuf
Couverture rigide
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Machine learning (ML) approaches have been extensively and successfully employed in various areas, like in economics, medical predictions, face recognition, credit card fraud detection, and spam filtering. There is clearly also the potential that ML techniques developed in Engineering and the Sciences will drastically increase the possibilities of analysis and accelerate the design to analysis time. With the use of ML techniques, coupled to conventional methods like finite element and digital twin technologies, new avenues of modeling and simulation can be opened but the potential of these ML techniques needs to still be fully harvested, with the methods developed and enhanced. The objective of this book is to provide an overview of ML in Engineering and the Sciences presenting fundamental theoretical ingredients with a focus on the next generation of computer modeling in Engineering and the Sciences in which the exciting aspects of machine learning are incorporated. The book is of value to any researcher and practitioner interested in research or applications of ML in the areas of scientific modeling and computer aided engineering. 464 pp. Englisch. N° de réf. du vendeur 9783031366437
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Machine Learning in Modeling and Simulation : Methods and Applications
Edité par
Springer International Publishing, 2023
ISBN 10 : 3031366433
ISBN 13 : 9783031366437
Neuf
Couverture rigide
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Machine learning (ML) approaches have been extensively and successfully employed in various areas, like in economics, medical predictions, face recognition, credit card fraud detection, and spam filtering. There is clearly also the potential that ML techniques developed in Engineering and the Sciences will drastically increase the possibilities of analysis and accelerate the design to analysis time. With the use of ML techniques, coupled to conventional methods like finite element and digital twin technologies, new avenues of modeling and simulation can be opened but the potential of these ML techniques needs to still be fully harvested, with the methods developed and enhanced. The objective of this book is to provide an overview of ML in Engineering and the Sciences presenting fundamental theoretical ingredients with a focus on the next generation of computer modeling in Engineering and the Sciences in which the exciting aspects of machine learning are incorporated. The book is of value to any researcher and practitioner interested in research or applications of ML in the areas of scientific modeling and computer aided engineering. N° de réf. du vendeur 9783031366437
Quantité disponible : 1 disponible(s)