Synopsis
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.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
Timon Rabczuk is Professor of Computational Mechanics at Bauhaus University Weimar. He has published more than 650 articles and 2 books. Timon is editor in Chief of CMC-Computers, Materials and Continua, associate editor of International Journal of Impact Engineering, Applied Physics A, International Journal of Mechanics and Materials in Design, Underground Space, Defense Technology and executive editor of FSCE-Frontiers of Structural and Civil Engineering. He is a member of the European Academy of Sciences and Arts. His research interests include Computational Science, Integrated Computational Materials Engineering and the numerical solution of partial differential equations. Klaus-Jürgen Bathe is Professor of Mechanical Engineering at the Massachusetts Institute of Technology. Professor Bathe is also the Founder of the company ADINA R & D recently acquired by Bentley Systems. He has published numerous articles, six textbooks, two books onhis life experiences, and is an Editor-in-Chief of the international journal Computers and Structures. He has been honored by the ASME, ASCE, U.S. National Academy of Engineering, through three awards at M.I.T., and through many honorary doctorates for his teaching, his pioneering and widely used fundamental contributions in computational mechanics, and for bridging the world between Academia and Industry.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Machine Learning in Modeling and Simulation
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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
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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
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Buch. Etat : Neu. Machine Learning in Modeling and Simulation | Methods and Applications | Timon Rabczuk (u. a.) | Buch | ix | Englisch | 2023 | Springer | EAN 9783031366437 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand. N° de réf. du vendeur 126986739
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Machine Learning in Modeling and Simulation
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Buch. Etat : Neu. 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 464 pp. Englisch. N° de réf. du vendeur 9783031366437
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Machine Learning in Modeling and Simulation: Methods and Applications
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Machine Learning in Modeling and Simulation : Methods and Applications
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Springer International Publishing, 2023
ISBN 10 : 3031366433
ISBN 13 : 9783031366437
Neuf
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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
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