Journal article 746 views 2233 downloads
Artificial Intelligence in Materials Modeling and Design
Archives of Computational Methods in Engineering, Volume: 28, Issue: 5, Pages: 3399 - 3413
Swansea University Author:
Adesola Ademiloye
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DOI (Published version): 10.1007/s11831-020-09506-1
Abstract
In recent decades, the use of artificial intelligence (AI) techniques in the field of materials modeling has received significant attention owing to their excellent ability to analyze a vast amount of data and reveal correlations between several complex interrelated phenomena. In this review paper,...
Published in: | Archives of Computational Methods in Engineering |
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ISSN: | 1134-3060 1886-1784 |
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Springer Science and Business Media LLC
2021
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URI: | https://cronfa.swan.ac.uk/Record/cronfa55399 |
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2021-09-07T15:18:13.9255745 v2 55399 2020-10-12 Artificial Intelligence in Materials Modeling and Design e37960ed89a7e3eaeba2201762626594 0000-0002-9741-6488 Adesola Ademiloye Adesola Ademiloye true false 2020-10-12 MEDE In recent decades, the use of artificial intelligence (AI) techniques in the field of materials modeling has received significant attention owing to their excellent ability to analyze a vast amount of data and reveal correlations between several complex interrelated phenomena. In this review paper, we summarize recent advances in the applications of AI techniques for numerical modeling of different types of materials. AI techniques such as machine learning and deep learning show great advantages and potential for predicting important mechanical properties of materials and reveal how changes in certain principal parameters affect the overall behavior of engineering materials. Furthermore, in this review, we show that the application of AI techniques can significantly help to improve the design and optimize the properties of future advanced engineering materials. Finally, a perspective on the challenges and prospects of the applications of AI techniques for material modeling is presented. Journal Article Archives of Computational Methods in Engineering 28 5 3399 3413 Springer Science and Business Media LLC 1134-3060 1886-1784 1 8 2021 2021-08-01 10.1007/s11831-020-09506-1 http://dx.doi.org/10.1007/s11831-020-09506-1 COLLEGE NANME Biomedical Engineering COLLEGE CODE MEDE Swansea University 2021-09-07T15:18:13.9255745 2020-10-12T12:19:44.2507921 Faculty of Science and Engineering School of Engineering and Applied Sciences - Biomedical Engineering J. S. Huang 1 K. M. Liew 2 Adesola Ademiloye 0000-0002-9741-6488 3 55399__18407__d37d7b9ce4bd4afcb7f8fe6283255003.pdf 55399.pdf 2020-10-12T12:23:11.7282199 Output 1474473 application/pdf Accepted Manuscript true 2021-10-11T00:00:00.0000000 true eng |
title |
Artificial Intelligence in Materials Modeling and Design |
spellingShingle |
Artificial Intelligence in Materials Modeling and Design Adesola Ademiloye |
title_short |
Artificial Intelligence in Materials Modeling and Design |
title_full |
Artificial Intelligence in Materials Modeling and Design |
title_fullStr |
Artificial Intelligence in Materials Modeling and Design |
title_full_unstemmed |
Artificial Intelligence in Materials Modeling and Design |
title_sort |
Artificial Intelligence in Materials Modeling and Design |
author_id_str_mv |
e37960ed89a7e3eaeba2201762626594 |
author_id_fullname_str_mv |
e37960ed89a7e3eaeba2201762626594_***_Adesola Ademiloye |
author |
Adesola Ademiloye |
author2 |
J. S. Huang K. M. Liew Adesola Ademiloye |
format |
Journal article |
container_title |
Archives of Computational Methods in Engineering |
container_volume |
28 |
container_issue |
5 |
container_start_page |
3399 |
publishDate |
2021 |
institution |
Swansea University |
issn |
1134-3060 1886-1784 |
doi_str_mv |
10.1007/s11831-020-09506-1 |
publisher |
Springer Science and Business Media LLC |
college_str |
Faculty of Science and Engineering |
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facultyofscienceandengineering |
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Faculty of Science and Engineering |
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facultyofscienceandengineering |
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Faculty of Science and Engineering |
department_str |
School of Engineering and Applied Sciences - Biomedical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Biomedical Engineering |
url |
http://dx.doi.org/10.1007/s11831-020-09506-1 |
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1 |
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0 |
description |
In recent decades, the use of artificial intelligence (AI) techniques in the field of materials modeling has received significant attention owing to their excellent ability to analyze a vast amount of data and reveal correlations between several complex interrelated phenomena. In this review paper, we summarize recent advances in the applications of AI techniques for numerical modeling of different types of materials. AI techniques such as machine learning and deep learning show great advantages and potential for predicting important mechanical properties of materials and reveal how changes in certain principal parameters affect the overall behavior of engineering materials. Furthermore, in this review, we show that the application of AI techniques can significantly help to improve the design and optimize the properties of future advanced engineering materials. Finally, a perspective on the challenges and prospects of the applications of AI techniques for material modeling is presented. |
published_date |
2021-08-01T04:09:34Z |
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1763753669265719296 |
score |
11.024221 |