Learning and intelligent optimization for material design innovation

Amir Mosavi, Timon Rabczuk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationLearning and Intelligent Optimization - 11th International Conference, LION 11, Revised Selected Papers
EditorsDmitri E. Kvasov, Yaroslav D. Sergeyev, Roberto Battiti, Roberto Battiti, Dmitri E. Kvasov, Yaroslav D. Sergeyev
PublisherSpringer Verlag
Number of pages6
ISBN (Print)9783319694030
Publication statusPublished - 2017
Event11th International Conference on Learning and Intelligent Optimization, LION 2017 - Nizhny Novgorod, Russian Federation
Duration: 2017 Jun 192017 Jun 21

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10556 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other11th International Conference on Learning and Intelligent Optimization, LION 2017
Country/TerritoryRussian Federation
CityNizhny Novgorod


  • Machine learning
  • Material design
  • Optimization

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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