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 -