Efficient Deep Learning for Gradient-Enhanced Stress Dependent Damage Model
This manuscript introduces a computational approach to micro-damage problems using deep learning for the prediction of loading deflection curves. The location of applied forces, dimensions of the specimen and material parameters are used as inputs of the process. The micro-damage is modelled with a...
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doaj-66a042c978ab41a49203464edaa076972020-11-25T02:37:27ZengMDPI AGApplied Sciences2076-34172020-04-01102556255610.3390/app10072556Efficient Deep Learning for Gradient-Enhanced Stress Dependent Damage ModelXiaoying Zhuang0L. C. Nguyen1Hung Nguyen-Xuan2Naif Alajlan3Timon Rabczuk4Division of Computational Mechanics, Ton Duc Thang University, Ho Chi Minh City, Viet NamInstitute for Continuum Mechanics, Leibniz Universität Hannover, Appelstr. 11, 30167 Hannover, GermanyCIRTech Institute, Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City, VietnamDepartment of Computer Engineering College of Computer and Information Sciences King Saud University, Riyadh 11543, Saudi ArabiaDepartment of Computer Engineering College of Computer and Information Sciences King Saud University, Riyadh 11543, Saudi ArabiaThis manuscript introduces a computational approach to micro-damage problems using deep learning for the prediction of loading deflection curves. The location of applied forces, dimensions of the specimen and material parameters are used as inputs of the process. The micro-damage is modelled with a gradient-enhanced damage model which ensures the well-posedness of the boundary value and yields mesh-independent results in computational methods such as FEM. We employ the Adam optimizer and Rectified linear unit activation function for training processes and research into the deep neural network architecture. The performance of our approach is demonstrated through some numerical examples including the three-point bending specimen, shear bending on L-shaped specimen and different failure mechanisms.https://www.mdpi.com/2076-3417/10/7/2556deep neural networkdeep learninggradient enhanced damagestress-level dependent damage model |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xiaoying Zhuang L. C. Nguyen Hung Nguyen-Xuan Naif Alajlan Timon Rabczuk |
spellingShingle |
Xiaoying Zhuang L. C. Nguyen Hung Nguyen-Xuan Naif Alajlan Timon Rabczuk Efficient Deep Learning for Gradient-Enhanced Stress Dependent Damage Model Applied Sciences deep neural network deep learning gradient enhanced damage stress-level dependent damage model |
author_facet |
Xiaoying Zhuang L. C. Nguyen Hung Nguyen-Xuan Naif Alajlan Timon Rabczuk |
author_sort |
Xiaoying Zhuang |
title |
Efficient Deep Learning for Gradient-Enhanced Stress Dependent Damage Model |
title_short |
Efficient Deep Learning for Gradient-Enhanced Stress Dependent Damage Model |
title_full |
Efficient Deep Learning for Gradient-Enhanced Stress Dependent Damage Model |
title_fullStr |
Efficient Deep Learning for Gradient-Enhanced Stress Dependent Damage Model |
title_full_unstemmed |
Efficient Deep Learning for Gradient-Enhanced Stress Dependent Damage Model |
title_sort |
efficient deep learning for gradient-enhanced stress dependent damage model |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2020-04-01 |
description |
This manuscript introduces a computational approach to micro-damage problems using deep learning for the prediction of loading deflection curves. The location of applied forces, dimensions of the specimen and material parameters are used as inputs of the process. The micro-damage is modelled with a gradient-enhanced damage model which ensures the well-posedness of the boundary value and yields mesh-independent results in computational methods such as FEM. We employ the Adam optimizer and Rectified linear unit activation function for training processes and research into the deep neural network architecture. The performance of our approach is demonstrated through some numerical examples including the three-point bending specimen, shear bending on L-shaped specimen and different failure mechanisms. |
topic |
deep neural network deep learning gradient enhanced damage stress-level dependent damage model |
url |
https://www.mdpi.com/2076-3417/10/7/2556 |
work_keys_str_mv |
AT xiaoyingzhuang efficientdeeplearningforgradientenhancedstressdependentdamagemodel AT lcnguyen efficientdeeplearningforgradientenhancedstressdependentdamagemodel AT hungnguyenxuan efficientdeeplearningforgradientenhancedstressdependentdamagemodel AT naifalajlan efficientdeeplearningforgradientenhancedstressdependentdamagemodel AT timonrabczuk efficientdeeplearningforgradientenhancedstressdependentdamagemodel |
_version_ |
1724795589101617152 |