Details
| Original language | English |
|---|---|
| Pages (from-to) | 21-30 |
| Number of pages | 10 |
| Journal | Finite Elements in Analysis and Design |
| Volume | 165 |
| Early online date | 20 Aug 2019 |
| Publication status | Published - Nov 2019 |
Abstract
We present a deep learning method to investigate the effect of flexoelectricity in nanostructures. For this purpose, deep neural network (DNN) algorithm is employed to map the relation between the inputs and the material response of interest. The DNN model is trained and tested making use of database that has been established by solving the governing equations of flexoelectricity using a NURBS-based IGA formulation at design points in the full probability space of the input parameters. Firstly, pure flexoelectric cantilever nanobeam is investigated under mechanical and electrical loading conditions. Then, structures of composite system constituted by two non-piezoelectric material phases are addressed in order to find the optimized topology with respect to the energy conversion factor. The results show promising capabilities of the proposed method, in terms of accuracy and computational efficiency. The deep learning method we used have produced superior optimal designs compared to the numerical methods. The findings of this study will be of profound interest to researcher involved further in the optimization and design of flexoelectric structures.
Keywords
- Deep neural network, Flexoelectricity, Isogeometric analysis (IGA), Machine learning, Piezoelectricity, Topology optimization
ASJC Scopus subject areas
- Mathematics(all)
- Analysis
- Engineering(all)
- General Engineering
- Computer Science(all)
- Computer Graphics and Computer-Aided Design
- Mathematics(all)
- Applied Mathematics
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In: Finite Elements in Analysis and Design, Vol. 165, 11.2019, p. 21-30.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - A novel deep learning based method for the computational material design of flexoelectric nanostructures with topology optimization
AU - Hamdia, K.M.
AU - Ghasemi, H.
AU - Bazi, Y.
AU - AlHichri, H.
AU - Alajlan, N.
AU - Rabczuk, T.
N1 - Funding Information: The authors extend their appreciation to the Distinguished Scientist Fellowship Program (DSFP) at King Saud University for funding this work.
PY - 2019/11
Y1 - 2019/11
N2 - We present a deep learning method to investigate the effect of flexoelectricity in nanostructures. For this purpose, deep neural network (DNN) algorithm is employed to map the relation between the inputs and the material response of interest. The DNN model is trained and tested making use of database that has been established by solving the governing equations of flexoelectricity using a NURBS-based IGA formulation at design points in the full probability space of the input parameters. Firstly, pure flexoelectric cantilever nanobeam is investigated under mechanical and electrical loading conditions. Then, structures of composite system constituted by two non-piezoelectric material phases are addressed in order to find the optimized topology with respect to the energy conversion factor. The results show promising capabilities of the proposed method, in terms of accuracy and computational efficiency. The deep learning method we used have produced superior optimal designs compared to the numerical methods. The findings of this study will be of profound interest to researcher involved further in the optimization and design of flexoelectric structures.
AB - We present a deep learning method to investigate the effect of flexoelectricity in nanostructures. For this purpose, deep neural network (DNN) algorithm is employed to map the relation between the inputs and the material response of interest. The DNN model is trained and tested making use of database that has been established by solving the governing equations of flexoelectricity using a NURBS-based IGA formulation at design points in the full probability space of the input parameters. Firstly, pure flexoelectric cantilever nanobeam is investigated under mechanical and electrical loading conditions. Then, structures of composite system constituted by two non-piezoelectric material phases are addressed in order to find the optimized topology with respect to the energy conversion factor. The results show promising capabilities of the proposed method, in terms of accuracy and computational efficiency. The deep learning method we used have produced superior optimal designs compared to the numerical methods. The findings of this study will be of profound interest to researcher involved further in the optimization and design of flexoelectric structures.
KW - Deep neural network
KW - Flexoelectricity
KW - Isogeometric analysis (IGA)
KW - Machine learning
KW - Piezoelectricity
KW - Topology optimization
UR - http://www.scopus.com/inward/record.url?scp=85070798257&partnerID=8YFLogxK
U2 - 10.1016/j.finel.2019.07.001
DO - 10.1016/j.finel.2019.07.001
M3 - Article
VL - 165
SP - 21
EP - 30
JO - Finite Elements in Analysis and Design
JF - Finite Elements in Analysis and Design
SN - 0168-874X
ER -