Details
| Original language | English |
|---|---|
| Pages (from-to) | 79-87 |
| Number of pages | 9 |
| Journal | Computers, Materials and Continua |
| Volume | 59 |
| Issue number | 1 |
| Publication status | Published - 2019 |
Abstract
In this study, machine learning representation is introduced to evaluate the flexoelectricity effect in truncated pyramid nanostructure under compression. A NonUniform Rational B-spline (NURBS) based IGA formulation is employed to model the flexoelectricity. We investigate 2D system with an isotropic linear elastic material under plane strain conditions discretized by 45×30 grid of B-spline elements. Six input parameters are selected to construct a deep neural network (DNN) model. They are the Young's modulus, two dielectric permittivity constants, the longitudinal and transversal flexoelectric coefficients and the order of the shape function. The outputs of interest are the strain in the stress direction and the electric potential due flexoelectricity. The dataset are generated from the forward analysis of the flexoelectric model. 80% of the dataset is used for training purpose while the remaining is used for validation by checking the mean squared error. In addition to the input and output layers, the developed DNN model is composed of four hidden layers. The results showed high predictions capabilities of the proposed method with much lower computational time in comparison to the numerical model.
Keywords
- Deep neural networks, Flexoelectricity, Isogeometric analysis, Machine learning prediction
ASJC Scopus subject areas
- Materials Science(all)
- Biomaterials
- Mathematics(all)
- Modelling and Simulation
- Engineering(all)
- Mechanics of Materials
- Computer Science(all)
- Computer Science Applications
- Engineering(all)
- Electrical and Electronic Engineering
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In: Computers, Materials and Continua, Vol. 59, No. 1, 2019, p. 79-87.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Computational machine learning representation for the flexoelectricity effect in truncated pyramid structures
AU - Hamdia, Khader M.
AU - Ghasemi, Hamid
AU - Zhuang, Xiaoying
AU - Alajlan, Naif
AU - Rabczuk, Timon
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
Y1 - 2019
N2 - In this study, machine learning representation is introduced to evaluate the flexoelectricity effect in truncated pyramid nanostructure under compression. A NonUniform Rational B-spline (NURBS) based IGA formulation is employed to model the flexoelectricity. We investigate 2D system with an isotropic linear elastic material under plane strain conditions discretized by 45×30 grid of B-spline elements. Six input parameters are selected to construct a deep neural network (DNN) model. They are the Young's modulus, two dielectric permittivity constants, the longitudinal and transversal flexoelectric coefficients and the order of the shape function. The outputs of interest are the strain in the stress direction and the electric potential due flexoelectricity. The dataset are generated from the forward analysis of the flexoelectric model. 80% of the dataset is used for training purpose while the remaining is used for validation by checking the mean squared error. In addition to the input and output layers, the developed DNN model is composed of four hidden layers. The results showed high predictions capabilities of the proposed method with much lower computational time in comparison to the numerical model.
AB - In this study, machine learning representation is introduced to evaluate the flexoelectricity effect in truncated pyramid nanostructure under compression. A NonUniform Rational B-spline (NURBS) based IGA formulation is employed to model the flexoelectricity. We investigate 2D system with an isotropic linear elastic material under plane strain conditions discretized by 45×30 grid of B-spline elements. Six input parameters are selected to construct a deep neural network (DNN) model. They are the Young's modulus, two dielectric permittivity constants, the longitudinal and transversal flexoelectric coefficients and the order of the shape function. The outputs of interest are the strain in the stress direction and the electric potential due flexoelectricity. The dataset are generated from the forward analysis of the flexoelectric model. 80% of the dataset is used for training purpose while the remaining is used for validation by checking the mean squared error. In addition to the input and output layers, the developed DNN model is composed of four hidden layers. The results showed high predictions capabilities of the proposed method with much lower computational time in comparison to the numerical model.
KW - Deep neural networks
KW - Flexoelectricity
KW - Isogeometric analysis
KW - Machine learning prediction
UR - http://www.scopus.com/inward/record.url?scp=85064858465&partnerID=8YFLogxK
U2 - 10.32604/cmc.2019.05882
DO - 10.32604/cmc.2019.05882
M3 - Article
AN - SCOPUS:85064858465
VL - 59
SP - 79
EP - 87
JO - Computers, Materials and Continua
JF - Computers, Materials and Continua
SN - 1546-2218
IS - 1
ER -