A deep energy method for functionally graded porous beams  

作　　者：Arvin MOJAHEDIN Mohammad SALAVATI Timon RABCZUK 

机构地区：[1]Institute of Structural Mechanics,Bauhaus-Universitat Weimar,Weimar 99423,Germany [2]Institute of Material Science and Technology,Department of Materials Engineering,Technische Universitat Berlin,Berlin 10623,Germany [3]Division of Computational Mechanics,Ton Due Thang University,Ho Chi Minh City,Vietnam [4]Faculty of Civil Engineering,Ton Due Thang University,Ho Chi Minh City,Vietnam

出　　处：《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》2021年第6期492-498,共7页浙江大学学报（英文版）A辑（应用物理与工程）

摘　　要：We present a deep energy method(DEM)to solve functionally graded porous beams.We use the EulerBernoulli assumptions with varying mechanical properties across the thickness.DEM is subsequently developed,and its performance is demonstrated by comparing the analytical solution,which was adopted from our previous work.The proposed method completely eliminates the need of a discretization technique,such as the finite element method,and optimizes the potential energy of the beam to train the neural network.Once the neural network has been trained,the solution is obtained in a very short amount of time.

关 键 词：Energy-based method Multilayer perceptron methodology Functionally graded porous materials Euler-Bernoulli beam theory 

分 类 号：O313[理学—一般力学与力学基础]