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An Importance Sampling Approach to the Estimation of Algorithm Performance in Automated Algorithm Design Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Learning and Intelligent Optimization for Material Design Innovation

Authors : Amir Mosavi, Timon Rabczuk

Published in: Learning and Intelligent Optimization

Publisher: Springer International Publishing

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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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Metadata
Title
Learning and Intelligent Optimization for Material Design Innovation
Authors
Amir Mosavi
Timon Rabczuk
Copyright Year
2017
Book
Learning and Intelligent Optimization

Print ISBN: 978-3-319-69403-0

Electronic ISBN: 978-3-319-69404-7

Copyright Year: 2017

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

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