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International Conference on Learning and Intelligent Optimization

LION 2017: Learning and Intelligent Optimization pp 358–363Cite as

Learning and Intelligent Optimization for Material Design Innovation

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10556))

Abstract

Learning and intelligent optimization (LION) techniques enable problem-specific solvers with vast potential applications in industry and business. This paper explores such potentials for material design innovation and presents a review of the state of the art and a proposal of a method to use LION in this context. The research on material design innovation is crucial for the long-lasting success of any technological sector and industry and it is a rapidly evolving field of challenges and opportunities aiming at development and application of multi-scale methods to simulate, predict and select innovative materials with high accuracy. The LION way is proposed as an adaptive solver toolbox for the virtual optimal design and simulation of innovative materials to model the fundamental properties and behavior of a wide range of multi-scale materials design problems.

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Author information

Authors and Affiliations

  1. Institute of Structural Mechanics, Bauhaus-Universitat Weimar, Marienstr.15, 99423, Weimar, Germany

    Amir Mosavi & Timon Rabczuk

  2. Department of Computer and Information Science, Norwegian University of Science and Technology, Sem Saelandsvei 9, 7491, Trondheim, Norway

    Amir Mosavi

Authors
  1. Amir Mosavi
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  2. Timon Rabczuk
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Corresponding authors

Correspondence to Amir Mosavi or Timon Rabczuk .

Editor information

Editors and Affiliations

  1. University of Trento, Trento, Italy

    Roberto Battiti

  2. University of Calabria, Rende, Italy

    Dmitri E. Kvasov

  3. University of Calabria, Rende, Italy

    Yaroslav D. Sergeyev

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Mosavi, A., Rabczuk, T. (2017). Learning and Intelligent Optimization for Material Design Innovation. In: Battiti, R., Kvasov, D., Sergeyev, Y. (eds) Learning and Intelligent Optimization. LION 2017. Lecture Notes in Computer Science(), vol 10556. Springer, Cham. https://doi.org/10.1007/978-3-319-69404-7_31

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  • DOI: https://doi.org/10.1007/978-3-319-69404-7_31

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69403-0

  • Online ISBN: 978-3-319-69404-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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