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An efficient multiscale surrogate modelling framework for composite materials considering progressive damage based on artificial neural networks

Shibo Yan, Xi Zou Orcid Logo, Mohammad Ilkhani, Arthur Jones

Composites Part B: Engineering, Volume: 194, Start page: 108014

Swansea University Author: Xi Zou Orcid Logo

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DOI (Published version): 10.1016/j.compositesb.2020.108014

Published in: Composites Part B: Engineering
ISSN: 1359-8368
Published: Elsevier BV 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa65259
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first_indexed 2024-04-10T11:01:30Z
last_indexed 2024-04-10T11:01:30Z
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recordtype SURis
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spelling v2 65259 2023-12-11 An efficient multiscale surrogate modelling framework for composite materials considering progressive damage based on artificial neural networks a9f66a1e56009848af57c0d174d08ffe 0000-0001-7436-7903 Xi Zou Xi Zou true false 2023-12-11 AERO Journal Article Composites Part B: Engineering 194 108014 Elsevier BV 1359-8368 Multiscale modelling; Progressive damage; Surrogate model; Artificial neural network 1 8 2020 2020-08-01 10.1016/j.compositesb.2020.108014 COLLEGE NANME Aerospace Engineering COLLEGE CODE AERO Swansea University This work is funded by the Clean Sky 2 Joint Undertaking under the European Union's Horizon 2020 research and innovation programme under grant agreement No 754581. The authors thank Prof. Shuguang Li (University of Nottingham) for his valuable suggestions. 2024-04-10T12:01:38.9869240 2023-12-11T10:19:41.6916971 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering Shibo Yan 1 Xi Zou 0000-0001-7436-7903 2 Mohammad Ilkhani 3 Arthur Jones 4
title An efficient multiscale surrogate modelling framework for composite materials considering progressive damage based on artificial neural networks
spellingShingle An efficient multiscale surrogate modelling framework for composite materials considering progressive damage based on artificial neural networks
Xi Zou
title_short An efficient multiscale surrogate modelling framework for composite materials considering progressive damage based on artificial neural networks
title_full An efficient multiscale surrogate modelling framework for composite materials considering progressive damage based on artificial neural networks
title_fullStr An efficient multiscale surrogate modelling framework for composite materials considering progressive damage based on artificial neural networks
title_full_unstemmed An efficient multiscale surrogate modelling framework for composite materials considering progressive damage based on artificial neural networks
title_sort An efficient multiscale surrogate modelling framework for composite materials considering progressive damage based on artificial neural networks
author_id_str_mv a9f66a1e56009848af57c0d174d08ffe
author_id_fullname_str_mv a9f66a1e56009848af57c0d174d08ffe_***_Xi Zou
author Xi Zou
author2 Shibo Yan
Xi Zou
Mohammad Ilkhani
Arthur Jones
format Journal article
container_title Composites Part B: Engineering
container_volume 194
container_start_page 108014
publishDate 2020
institution Swansea University
issn 1359-8368
doi_str_mv 10.1016/j.compositesb.2020.108014
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering
document_store_str 0
active_str 0
published_date 2020-08-01T12:01:36Z
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score 11.037056

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