Journal article 571 views
An efficient multiscale surrogate modelling framework for composite materials considering progressive damage based on artificial neural networks
Shibo Yan,
Xi Zou 
,
Mohammad Ilkhani,
Arthur Jones
,
Mohammad Ilkhani,
Arthur Jones
Composites Part B: Engineering, Volume: 194, Start page: 108014
Swansea University Author:
Xi Zou
Full text not available from this repository: check for access using links below.
DOI (Published version): 10.1016/j.compositesb.2020.108014
Abstract
An efficient multiscale surrogate modelling framework for composite materials considering progressive damage based on artificial neural networks
| Published in: | Composites Part B: Engineering |
|---|---|
| ISSN: | 1359-8368 |
| Published: |
Elsevier BV
2020
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa65259 |
| Keywords: |
Multiscale modelling; Progressive damage; Surrogate model; Artificial neural network |
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| College: |
Faculty of Science and Engineering |
| Funders: |
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. |
| Start Page: |
108014 |