A novel deep learning based method for the computational material design of flexoelectric nanostructures with topology optimization

authored by: K.M. Hamdia, H. Ghasemi, Y. Bazi, H. AlHichri, N. Alajlan, T. Rabczuk
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.
Organisation(s): Institute of Continuum Mechanics
External Organisation(s): Bauhaus-Universität Weimar
Arak University of Technology
King Saud University
Type: Article
Journal: Finite Elements in Analysis and Design
Volume: 165
Pages: 21-30
No. of pages: 10
ISSN: 0168-874X
Publication date: 11.2019
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Analysis, General Engineering, Computer Graphics and Computer-Aided Design, Applied Mathematics
Electronic version(s): https://doi.org/10.1016/j.finel.2019.07.001 (Access: Closed)
: Details in the research portal "Research@Leibniz University"

BibTeX

@article{ea0e1c54b2ab4c6dbb0475586994df93,
title = "A novel deep learning based method for the computational material design of flexoelectric nanostructures with topology optimization",
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",
author = "K.M. Hamdia and H. Ghasemi and Y. Bazi and H. AlHichri and N. Alajlan and T. Rabczuk",
note = "Funding Information: The authors extend their appreciation to the Distinguished Scientist Fellowship Program (DSFP) at King Saud University for funding this work.",
year = "2019",
month = nov,
doi = "10.1016/j.finel.2019.07.001",
language = "English",
volume = "165",
pages = "21--30",
journal = "Finite Elements in Analysis and Design",
issn = "0168-874X",
publisher = "Elsevier",
}