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Machine learning in modeling and sim...

Rabczuk, Timon.

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Machine learning in modeling and simulation = methods and applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning in modeling and simulation/ edited by Timon Rabczuk, Klaus-Jürgen Bathe.
Reminder of title:	methods and applications /
other author:	Rabczuk, Timon.
Published:	Cham :Springer International Publishing : : 2023.,
Description:	ix, 451 p. :ill. (chiefly color), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-3-031-36644-4
ISBN:	9783031366444

Machine learning in modeling and simulation = methods 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

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

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Machine learning in modeling and simulation = methods and applications /
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490 1 $a Computational methods in engineering & the sciences, $x 2662-4877
505 0 $a 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.
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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