Skip to main content
Springer Nature Link
Log in

Machine Learning in Modeling and Simulation

Methods and Applications

  • Book
  • © 2023

Overview

Editors:
  1. Timon Rabczuk

    1. Bauhaus University, Weimar, Weimar, Germany

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  2. Klaus-Jürgen Bathe

    1. Massachusetts Institute of Technology (MIT), Cambridge, USA

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • Comprehensive state-of-the-art book on scientific machine learning approaches in modelling & simulation
  • Covers the wide range of PDEs, uncertainty, optimization, inverse analysis, constitutive modelling & material design
  • Focuses on engineering applications in modelling & material design

Part of the book series: Computational Methods in Engineering & the Sciences (CMES)

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

eBook USD 159.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

About this book

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.



Keywords

Table of contents (12 chapters)

  1. Front Matter

    Pages i-ix
    Download chapter PDF

  2. Machine Learning in Computer Aided Engineering

    • Francisco J. Montáns, Elías Cueto, Klaus-Jürgen Bathe
    Pages 1-83Open Access

  3. Artificial Neural Networks

    • K. Worden, G. Tsialiamanis, E. J. Cross, T. J. Rogers
    Pages 85-119

  4. Gaussian Processes

    • T. J. Rogers, J. Mclean, E. J. Cross, K. Worden
    Pages 121-147

  5. Machine Learning Methods for Constructing Dynamic Models From Data

    • J. Nathan Kutz
    Pages 149-178

  6. Physics-Informed Neural Networks: Theory and Applications

    • Cosmin Anitescu, Burak İsmail Ateş, Timon Rabczuk
    Pages 179-218

  7. Physics-Informed Deep Neural Operator Networks

    • Somdatta Goswami, Aniruddha Bora, Yue Yu, George Em Karniadakis
    Pages 219-254

  8. Digital Twin for Dynamical Systems

    • Tapas Tripura, Shailesh Garg, Souvik Chakraborty
    Pages 255-296

  9. Reduced Order Modeling

    • Zulkeefal Dar, Joan Baiges, Ramon Codina
    Pages 297-339

  10. Regression Models for Machine Learning

    • Pengfei Wei, Michael Beer
    Pages 341-371

  11. 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

  12. 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

  13. Machine Learning Interatomic Potentials: Keys to First-Principles Multiscale Modeling

    • Bohayra Mortazavi
    Pages 427-451
Back to top

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.

Bibliographic Information

Publish with us

Policies and ethics

Back to top