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Machine learning in modeling and simulationmethods and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning in modeling and simulationedited by Timon Rabczuk, Klaus-Jürgen Bathe.
其他題名:	methods and applications /
其他作者:	Rabczuk, Timon.
出版者:	Cham :Springer International Publishing :2023.
面頁冊數:	ix, 451 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Machine learning.
電子資源:	https://doi.org/10.1007/978-3-031-36644-4
ISBN:	9783031366444$q(electronic bk.)

Machine learning in modeling and simulationmethods and applications /
Machine learning in modeling and simulationmethods and applications /[electronic resource] :edited by Timon Rabczuk, Klaus-Jürgen Bathe. - Cham :Springer International Publishing :2023. - ix, 451 p. :ill. (chiefly color), digital ;24 cm. - Computational methods in engineering & the sciences,2662-4877. - Computational methods in engineering & the sciences..

Machine Learning in Computer-Aided Engineering -- Artificial Neural Networks -- Gaussian Processes -- Machine Learning Methods for Constructing Dynamic Models from Data -- Physics-Informed Neural Networks: Theory and Applications -- Physics-Informed Deep Neural Operator Networks -- Digital Twin for Dynamical Systems -- Reduced Order Modeling -- Regression Models for Machine Learning -- Overview on Machine Learning Assisted Topology Optimization Methodologies -- Mixed-variable Concurrent Material, Geometry and Process Design in Integrated Computational Materials Engineering -- Machine Learning Interatomic Potentials: Keys to First-principles Multiscale Modeling.

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.

ISBN: 9783031366444$q(electronic bk.)

Standard No.: 10.1007/978-3-031-36644-4doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Machine learning in modeling and simulationmethods and applications /
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520 $a 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.
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