标题： 用于随机介质中渗流弹性问题的有效性质预测的机器学习方法 显示英文标题

标题： Machine learning for accelerating effective property prediction for poroelasticity problem in stochastic media

摘要： 在本文中，我们考虑具有随机特性的多孔弹性问题的数值均质化。 所提出的方法基于构建深度神经网络（DNN），以快速计算问题粗网格近似下的有效性质。 我们在选定的局部微观尺度随机场和宏观尺度特征（渗透率和弹性张量）的集合上训练神经网络。 我们通过卷积神经网络（CNN）构建一种深度学习方法，以学习随机场与有效性质之间的映射。 给出了二维和三维模型问题的数值结果，结果表明所提出的方法能够提供快速且准确的有效性质预测。 显示英文摘要

摘要： In this paper, we consider a numerical homogenization of the poroelasticity problem with stochastic properties. The proposed method based on the construction of the deep neural network (DNN) for fast calculation of the effective properties for a coarse grid approximation of the problem. We train neural networks on the set of the selected realizations of the local microscale stochastic fields and macroscale characteristics (permeability and elasticity tensors). We construct a deep learning method through convolutional neural network (CNN) to learn a map between stochastic fields and effective properties. Numerical results are presented for two and three-dimensional model problems and show that proposed method provide fast and accurate effective property predictions.