论文标题
用于异质材料均质化的三维卷积神经网络（3D-CNN）
Three-dimensional convolutional neural network (3D-CNN) for heterogeneous material homogenization
论文作者
论文摘要
均质化是一种在多尺度计算科学和工程中常用的技术，用于预测异质材料的集体响应并提取有效的机械性能。在本文中，提出了三维深度卷积神经网络（3D-CNN），以预测具有随机球形夹杂物的代表性体积元素（RVE）的有效材料特性。通过计算均质化方法生成的高保真数据集用于训练3D-CNN模型。在看不见的数据上，受过训练的网络的推论结果表明，该网络能够捕获RVE的微观结构特征，并可以准确预测有效的刚度和泊松比。关于计算效率，不确定性量化和模型的可传递性，3D-CNN对基于常规有限元的均质化的好处进行了序列。我们发现3D-CNN方法的显着特征使其成为使用快速产品设计迭代和有效的不确定性定量促进材料设计的潜在替代方案。
Homogenization is a technique commonly used in multiscale computational science and engineering for predicting collective response of heterogeneous materials and extracting effective mechanical properties. In this paper, a three-dimensional deep convolutional neural network (3D-CNN) is proposed to predict the effective material properties for representative volume elements (RVEs) with random spherical inclusions. The high-fidelity dataset generated by a computational homogenization approach is used for training the 3D-CNN models. The inference results of the trained networks on unseen data indicate that the network is capable of capturing the microstructural features of RVEs and produces an accurate prediction of effective stiffness and Poisson's ratio. The benefits of the 3D-CNN over conventional finite-element-based homogenization with regard to computational efficiency, uncertainty quantification and model's transferability are discussed in sequence. We find the salient features of the 3D-CNN approach make it a potentially suitable alternative for facilitating material design with fast product design iteration and efficient uncertainty quantification.
