In this paper,a data-driven method to model the three-dimensional engineering structure under the cyclic load with the one-dimensional stress-strain data is proposed.In this method,one-dimensional stress-strain data o...In this paper,a data-driven method to model the three-dimensional engineering structure under the cyclic load with the one-dimensional stress-strain data is proposed.In this method,one-dimensional stress-strain data obtained under uniaxial load and different loading history is learned offline by gate recurrent unit(GRU)network.The learned constitutive model is embedded into the general finite element framework through data expansion from one dimension to three dimensions,which can perform stress updates under the three-dimensional setting.The proposed method is then adopted to drive numerical solutions of boundary value problems for engineering structures.Compared with direct numerical simulations using the J2 plasticity model,the stress-strain response of beam structure with elastoplastic materials under forward loading,reverse loading and cyclic loading were predicted accurately.Loading path dependent response of structure was captured and the effectiveness of the proposed method is verified.The shortcomings of the proposed method are also discussed.展开更多
针对生成对抗网络(GAN)需要大量训练数据及点云数据稀缺且获取难度大的问题,提出一种基于少量数据集的三维点云生成模型。该模型首先通过重采样和水平旋转的方法实现数据增强,使第一级网络能够生成具有多样性的低分辨率点云;其次通过确...针对生成对抗网络(GAN)需要大量训练数据及点云数据稀缺且获取难度大的问题,提出一种基于少量数据集的三维点云生成模型。该模型首先通过重采样和水平旋转的方法实现数据增强,使第一级网络能够生成具有多样性的低分辨率点云;其次通过确保低分辨率点云与高分辨率点云之间的对应关系,实现点云的超分辨率生成;最后实现生成具有多样性的高分辨率点云。实验结果表明,在ShapeNet Part(ShapeNet Part Segmentation Dataset)数据集上,该模型的JS散度相较于Tree-GAN的JS散度下降了0.416,证明其性能优于Tree-GAN。展开更多
文摘In this paper,a data-driven method to model the three-dimensional engineering structure under the cyclic load with the one-dimensional stress-strain data is proposed.In this method,one-dimensional stress-strain data obtained under uniaxial load and different loading history is learned offline by gate recurrent unit(GRU)network.The learned constitutive model is embedded into the general finite element framework through data expansion from one dimension to three dimensions,which can perform stress updates under the three-dimensional setting.The proposed method is then adopted to drive numerical solutions of boundary value problems for engineering structures.Compared with direct numerical simulations using the J2 plasticity model,the stress-strain response of beam structure with elastoplastic materials under forward loading,reverse loading and cyclic loading were predicted accurately.Loading path dependent response of structure was captured and the effectiveness of the proposed method is verified.The shortcomings of the proposed method are also discussed.
文摘针对生成对抗网络(GAN)需要大量训练数据及点云数据稀缺且获取难度大的问题,提出一种基于少量数据集的三维点云生成模型。该模型首先通过重采样和水平旋转的方法实现数据增强,使第一级网络能够生成具有多样性的低分辨率点云;其次通过确保低分辨率点云与高分辨率点云之间的对应关系,实现点云的超分辨率生成;最后实现生成具有多样性的高分辨率点云。实验结果表明,在ShapeNet Part(ShapeNet Part Segmentation Dataset)数据集上,该模型的JS散度相较于Tree-GAN的JS散度下降了0.416,证明其性能优于Tree-GAN。
