Machine Learning in Modeling and Simulation: Methods and Applications (Computational Methods in Engineering & the Sciences)

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Description

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.

Table of contents (12 chapters)
Front Matter
Pages i-ix
Machine Learning in Computer Aided Engineering
Francisco J. Montáns, Elías Cueto, Klaus-Jürgen Bathe
Pages 1-83
Artificial Neural Networks
K. Worden, G. Tsialiamanis, E. J. Cross, T. J. Rogers
Pages 85-119
Gaussian Processes
T. J. Rogers, J. Mclean, E. J. Cross, K. Worden
Pages 121-147
Machine Learning Methods for Constructing Dynamic Models From Data
J. Nathan Kutz
Pages 149-178
Physics-Informed Neural Networks: Theory and Applications
Cosmin Anitescu, Burak İsmail Ateş, Timon Rabczuk
Pages 179-218
Physics-Informed Deep Neural Operator Networks
Somdatta Goswami, Aniruddha Bora, Yue Yu, George Em Karniadakis
Pages 219-254
Digital Twin for Dynamical Systems
Tapas Tripura, Shailesh Garg, Souvik Chakraborty
Pages 255-296
Reduced Order Modeling
Zulkeefal Dar, Joan Baiges, Ramon Codina
Pages 297-339
Regression Models for Machine Learning
Pengfei Wei, Michael Beer
Pages 341-371
Overview on Machine Learning Assisted Topology Optimization Methodologies
Ilias Chamatidis, Manos Stoumpos, George Kazakis, Nikos Ath. Kallioras, Savvas Triantafyllou, Vagelis Plevris et al.
Pages 373-394
Mixed-Variable Concurrent Material, Geometry, and Process Design in Integrated Computational Materials Engineering
Tianyu Huang, Marisa Bisram, Yang Li, Hongyi Xu, Danielle Zeng, Xuming Su et al.
Pages 395-426
Machine Learning Interatomic Potentials: Keys to First-Principles Multiscale Modeling
Bohayra Mortazavi
Pages 427-451
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Editors and Affiliations
Bauhaus University, Weimar, Weimar, Germany
Timon Rabczuk

Massachusetts Institute of Technology (MIT), Cambridge, USA
Klaus-Jürgen Bathe

About the editors
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.

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