Convolutional neural network for fast prediction of the effective properties of domains with random inclusions
Convolutional neural network for fast prediction of the effective properties of domains with random inclusions
复制标题
DOI：
10.1088/1742-6596/1158/4/042034
复制
发表时间：
2019-02
期刊：
影响因子：
--
通讯作者：
M. Vasilyeva;A. Tyrylgin
中科院分区：
文献类型：
--
作者：
M. Vasilyeva;A. Tyrylgin
我们考虑一个非均匀区域的随机包含在二维和三维配方。为了生成训练和测试数据集，我们数值计算了给定几何形状的异构域的有效属性。我们构建了一个机器学习方法来学习局部异构几何和有效属性之间的映射。我们提出的数值结果预测的有效属性的2D和3D模型问题。
We consider a heterogeneous domain with random inclusions in two-dimensional and three-dimensional formulations. For generation of the train and test datasets, we numerically calculate the effective properties for a given geometry of the heterogeneous domain. We construct a machine learning method to learn a map between local heterogeneous geometries and effective properties. We present numerical results for prediction of the effective properties for 2D and 3D model problems.