國立虎尾科技大學 |

Machine learning in modeling and simulation = methods and applications /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Machine learning in modeling and simulation/ edited by Timon Rabczuk, Klaus-Jürgen Bathe.
其他題名:	methods and applications /
其他作者:	Bathe, Klaus-Jurgen.
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:	Applied Dynamical Systems. -
電子資源:	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:

1366186
Applied Dynamical Systems.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Machine learning in modeling and simulation = methods and applications /
LDR:02940nam a2200337 a 4500 001 1117701
003 DE-He213
005 20231003184024.0
006 m d
007 cr nn 008maaau
008 240126s2023 sz s 0 eng d
020 $a 9783031366444 $q (electronic bk.)
020 $a 9783031366437 $q (paper)
024 7 $a 10.1007/978-3-031-36644-4 $2 doi
035 $a 978-3-031-36644-4
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .M149 2023
245 0 0 $a Machine learning in modeling and simulation $h [electronic resource] : $b methods and applications / $c edited by Timon Rabczuk, Klaus-Jürgen Bathe.
260 $a Cham : $c 2023. $b Springer International Publishing : $b Imprint: Springer,
300 $a ix, 451 p. : $b ill. (chiefly color), digital ; $c 24 cm.
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.
650 2 4 $a Applied Dynamical Systems. $3 1366186
650 2 4 $a Engineering Mechanics. $3 1366277
650 1 4 $a Computational Intelligence. $3 768837
650 0 $a Engineering $x Data processing. $3 560191
650 0 $a Science $x Data processing. $3 528623
650 0 $a Artificial intelligence $x Engineering applications. $3 889372
650 0 $a Machine learning. $3 561253
700 1 $a Bathe, Klaus-Jurgen. $3 671318
700 1 $a Rabczuk, Timon. $e editor. $3 1286922
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
830 0 $a Computational methods in engineering & the sciences. $3 1431557
856 4 0 $u https://doi.org/10.1007/978-3-031-36644-4
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)