[1]王章骏,许 平,王春彭,等. 有限元位移解的生成式对抗网络替代方法[J].控制与信息技术,2019,(06):1.[doi:10.13889/j.issn.2096-5427.2019.06.300]
 WANG Zhangjun,XU Ping,WANG Chunpeng,et al. A Generative Adversarial Network Approach to Estimate Finite Element Displacement[J].High Power Converter Technology,2019,(06):1.[doi:10.13889/j.issn.2096-5427.2019.06.300]
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 有限元位移解的生成式对抗网络替代方法()
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《控制与信息技术》[ISSN:2095-3631/CN:43-1486/U]

卷:
期数:
2019年06期
页码:
1
栏目:
出版日期:
2019-12-05

文章信息/Info

Title:
 A Generative Adversarial Network Approach to Estimate Finite Element Displacement
作者:
 王章骏许 平王春彭赵紫亮蔡隽堃
 (中南大学 交通运输工程学院, 湖南 长沙 410075)
Author(s):
 WANG Zhangjun XU Ping WANG Chunpeng ZHAO Ziliang CAI Junkun
 ( School of Traffic & Transportation Engineering, Central South University, Changsha, Hunan 410075, China )
关键词:
 深度学习生成式对抗网络有限元法位移解
Keywords:
 deep learning GAN(generative adversarial network) finite element displacement
分类号:
TU2
DOI:
10.13889/j.issn.2096-5427.2019.06.300
文献标志码:
A
摘要:
 有限元方法存在求解计算复杂、依赖于网格划分和材料本构关系的缺点。为探索有限元法之外的求解方法,可将位移响应求解过程视为一个给定条件的图片生成过程,从而绕过物理方法求解。文章在生成式对抗网络(GAN)的基础上,结合卷积生成式对抗网络(DCGAN)和条件生成式对抗网络(CGAN),提出并训练了一种代替有限元方法直接求得二维平面位移解的深度学习模型,使得生成的位移分布接近于用有限元法所得的位移分布。仿真结果显示,本模型不仅能够得到位移解的大致分布,而且计算耗时也低于有限元法的,验证了通过GAN 求解位移响应的可行性。
Abstract:
 The finite element analysis(FEA) usually requires complex procedures to set up, depends on constitutive relation to obtaining final simulation results. In order to explore solutions other than the finite element method, the displacement response is considered as a picture generation process with given conditions, bypassing the physical method. Based on GAN, combined DCGAN and CGAN, a deep learning model was proposed to directly obtain the displacement solution of two-dimensional plane instead of the finite element method. The proposed model is trained to make the generated displacement distribution close to FEA. The results show that the
approximate distribution of displacement can be obtained by this model, and the calculation time is also lower than that of the FEA, which verifies the feasibility of solving displacement response by GAN.

参考文献/References:

 [1] 刘鸿文.材料力学(I)[M]. 5 版.北京:高等教育出版社, 2011.
[2] BELYTSCHKO T, BLACK T. Elastic crack growth in finite elements
with minimal remeshing[J]. International Journal for Numerical
Methods in Engineering, 1999, 45(5): 601-620.
[3] DUARTE C A, BABUSKA I, ODEN J T. Generalized finite element
methods for three-dimensional structural mechanics problems[J].
Computer & Structures, 2000, 77(2): 215-232.
[4] BEISSEL S, BELYTSCHKO T. Nodal integration of the element-free
Galerkin method[J].Computer Methods in Applied Mechanics and
Engineering, 1996,139(1-4): 49-74.
[5] CHENG Y M, ZHANG Y H, CHEN W S. Wilson non-conforming
element in numerical manifold method[J]. Commun. Numer. Meth,
2002, 18(12): 877-884.
[6] 侯宇青阳, 全吉成, 王宏伟. 深度学习发展综述[J]. 舰船电子工程,
2017, 37(4): 5-9.
[7] GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al.
Generative Adversarial Nets[C]// International Conference on Neural
Information Processing Systems. MIT Press, 2014.
[8] LIANG L, LIU M, MARTIN C, et al. A deep learning approach to
estimate stress distribution: a fast and accurate surrogate of finiteelement
analysis[J/OL]. Journal of The Royal Society Interface,
2018, 15(138)[2019-08-01].https://doi.org/10.1098/rsif.2017.0844.
[9] MIRZA M, OSINDERO S. Conditional Generative Adversarial
Nets[EB/OL]. (2014-11-06)[2019-08-01].https://arxiv.org/
abs/1411.1784.
[10] R A D F O R D A , M E T Z L , C H I N TA L A S . U n s u p e r v i s e d
Representation Learning with Deep Convolutional Generative
Adversarial Net-works[C]//4th International Conference on Learning
Representations. San Juan, Puerto Rico,2016.
[11] KRIZHEVSKY A, SUTSKEVER I, HINTON G. ImageNet
Classification with Deep Convolutional Neural Networks[C]// NIPS.
Curran Associates Inc. 2012.
[12] IOFFE S, SZEGEDY C. Batch Normalization: Accelerating Deep
Network Training by Reducing Internal Covariate Shift[EB/OL].
(2015-02-11) [2019-08-01]. https://arxiv.org/abs/1502.03167.
[13] GLOROT X, BORDES A, BENGIO Y. Deep sparse rectifier neural
networks[C]// Proceedings of the 14th International Conference on
Artificial Intelligence and Statistics. Ft. Lauderdale, 2011: 315-323.
[14] MAAS A, HANNUN A, NG A, et al. Rectifier nonlinearities improve
neural network acoustic models[C]// Proc. ICML. Atlanta, 2013,
30(1): 3.
[15] 曾攀. 有限元分析及应用[M]. 北京:清华大学出版社, 2004.
[16] ABADI M , AGARWAL A, BARHAM P, et al. TensorFlow: largescale
machine learning on heterogeneous distributed systems[EB/
OL].(2016-03-14)[2019-08-01]. https://arxiv.org/abs/1603.04467v1.
[17] ISOLA P, ZHU J Y, ZHOU T, et al. Image-to-Image Translation with
Conditional Adversarial Networks[C]// The IEEE Conference on
Computer Vision and Pattern Recognition (CVPR). Hawaii , 2017.
[18] K I N G M A D P, BA J . A d a m : A M e t h o d f o r S t o c h a s t i c
Optimization[C]// ICLR 2015. Ithaca NY, 2015.

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备注/Memo

备注/Memo:
 收稿日期:2019-08-07
作者简介:王章骏(1995—),男,在读硕士研究生,研究方向为轨道车辆耐撞性;
许平(1971—),男,博士,教授,主要从事轨道车辆耐撞性方面研究。
基金项目:国家重点研发计划项目(2016YFB1200505-016);国家自然科学基金项目(51675537)
更新日期/Last Update: 2019-12-03