Computational Machine Learning Representation for the Flexoelectricity Effect in Truncated Pyramid Structures

• 作者: Hamid Ghasemi Khader M.Hamdia Naif Alajlan Timon Rabczuk Xiaoying Zhuang
• 来源期刊: 计算机、材料和连续体(英文)
• 学科: 数学
• 关键词: FLEXOELECTRICITY Isogeometric analysis Machine learning prediction deep NEURAL networks
• 年，卷（期）: 2019,（4）
• 页码范围: 79-87
• 页数: 9页
• 分类号: O17

摘要

In this study,machine learning representation is introduced to evaluate the flexoelectricity effect in truncated pyramid nanostructure under compression.A Non-Uniform 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.