[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):57-62.[doi:10.13889/j.issn.2096-5427.2019.06.300]
 WANG Zhangjun,XU Ping,WANG Chunpeng,et al.A Generative Adversarial Network Approach to EstimateFinite Element Displacement[J].High Power Converter Technology,2019,(06):57-62.[doi:10.13889/j.issn.2096-5427.2019.06.300]
点击复制

有限元位移解的生成式对抗网络替代方法()
分享到:

《控制与信息技术》[ISSN:2095-3631/CN:43-1486/U]

卷:
期数:
2019年06期
页码:
57-62
栏目:
人工智能技术与应用
出版日期:
2019-12-05

文章信息/Info

Title:
A Generative Adversarial Network Approach to EstimateFinite Element Displacement
文章编号:
2096-5427(2019)06-0057-06
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 generative adversarial network(GAN) finite element displacement solution
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, and depends on constitutive relation to obtain 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 generative adversarial network(GAN), and combined with 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 finite-element 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] RADFORD A, METZ L, CHINTALA S. Unsupervised 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: large-scale 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] KINGMA D P, BA J. Adam: A Method for Stochastic Optimization[C]// ICLR 2015. Ithaca NY, 2015.

相似文献/References:

[1]熊群芳,林 军,刘 悦,等.深度学习研究现状及其在轨道交通领域的应用[J].控制与信息技术,2018,(02):1.[doi:10.13889/j.issn.2096-5427.2018.02.001]
 XIONG Qunfang,LIN Jun,LIU Yue,et al.Deep Learning and Its Application in the Field of Rail Transit[J].High Power Converter Technology,2018,(06):1.[doi:10.13889/j.issn.2096-5427.2018.02.001]
[2]熊群芳,林 军,岳 伟,等. 基于深度学习的疲劳驾驶状态检测方法[J].控制与信息技术,2018,(06):1.[doi:10.13889/j.issn.2096-5427.2018.06.400]
 XIONG Qunfang,LIN Jun,YUE Wei,et al. A Method of Fatigue Driving State Detection Based on Deep Learning[J].High Power Converter Technology,2018,(06):1.[doi:10.13889/j.issn.2096-5427.2018.06.400]
[3]熊群芳,林 军,岳 伟.基于深度学习的疲劳驾驶状态检测方法[J].控制与信息技术,2018,(06):91.[doi:10.13889/j.issn.2096-5427.2018.06.400]
 XIONG Qunfang,LIN Jun,YUE Wei.A Method of Fatigue Driving State Detection Based on Deep Learning[J].High Power Converter Technology,2018,(06):91.[doi:10.13889/j.issn.2096-5427.2018.06.400]
[4]丁 驰,林 军,游 俊,等.基于深度学习的手势识别方法[J].控制与信息技术,2018,(06):96.[doi:10.13889/j.issn.2096-5427.2018.06.016]
 DING Chi,LIN Jun,YOU Jun,et al.A Gesture Recognition Method Based on Deep Learning[J].High Power Converter Technology,2018,(06):96.[doi:10.13889/j.issn.2096-5427.2018.06.016]
[5]刘 悦,林 军,游 俊.语音识别技术在车载领域的应用及发展[J].控制与信息技术,2019,(02):1.[doi:10.13889/j.issn.2096-5427.2019.02.001]
 LIU Yue,LIN Jun,YOU Jun.Application and Development of Automatic Speech Recognition in Vehicle Field[J].High Power Converter Technology,2019,(06):1.[doi:10.13889/j.issn.2096-5427.2019.02.001]
[6]高 群,朱 均,王芊芊,等. 基于鱼眼图像的目标检测算法研究[J].控制与信息技术,2019,(03):1.[doi:10.13889/j.issn.2096-5427.2019.03.100]
 GAO Qun,ZHU Jun,WANG Qianqian,et al.Research on the Object Detection Algorithm Based on Fisheye Image[J].High Power Converter Technology,2019,(06):1.[doi:10.13889/j.issn.2096-5427.2019.03.100]
[7]高 群,朱 均,王芊芊,等.基于鱼眼图像的目标检测算法研究[J].控制与信息技术,2019,(03):43.[doi:10.13889/j.issn.2096-5427.2019.03.100]
 GAO Qun,ZHU Jun,WANG Qianqian,et al.Research on the Object Detection Algorithm Based on Fisheye Image[J].High Power Converter Technology,2019,(06):43.[doi:10.13889/j.issn.2096-5427.2019.03.100]
[8]齐 航,袁健全,李 磊,等. 基于深度学习的红外烟幕区域分割技术[J].控制与信息技术,2019,(04):1.[doi:10.13889/j.issn.2096-5427.2019.04.400]
 QI Hang,YUAN Jianquan,LI Lei,et al. A Method of Smoke Area Segmentation for Infrared Images Based on Deep Learning[J].High Power Converter Technology,2019,(06):1.[doi:10.13889/j.issn.2096-5427.2019.04.400]
[9]齐 航,袁健全,李 磊,等.基于深度学习的红外烟幕区域分割技术[J].控制与信息技术,2019,(04):18.[doi:10.13889/j.issn.2096-5427.2019.04.400]
 QI Hang,YUAN Jianquan,LI Lei,et al.A Method of Smoke Area Segmentation for Infrared Images Based on Deep Learning[J].High Power Converter Technology,2019,(06):18.[doi:10.13889/j.issn.2096-5427.2019.04.400]
[10]熊群芳,林 军,岳 伟,等. 基于深度学习的驾驶员打电话行为检测方法[J].控制与信息技术,2019,(06):1.[doi:10.13889/j.issn.2096-5427.2019.06.400]
 XIONG Qunfang,LIN Jun,YUE Wei,et al. A Driver Calling Behavior Detection Method Based on Deep Learning[J].High Power Converter Technology,2019,(06):1.[doi:10.13889/j.issn.2096-5427.2019.06.400]
[11]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]

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(51675537)
更新日期/Last Update: 2019-12-25