裂缝性介质多尺度深度学习模型

结合人工神经网络建立裂缝介质多尺度深度学习流动模型.基于一套粗网格和一套细网格，通过在粗网格上训练数据，多尺度神经网络能够以较少的自由度训练出准确的神经网络.并在粗网格上通过求解局部流动问题获得多尺度基函数，结合神经网络进一步得到精细网格的解.基于离散裂缝的流动方程可视为多层网络，网络层数依赖于求解时间步数.阐述裂缝介质多尺度机器学习数值计算格式的建立，介绍如何使用多尺度算法构建离散裂缝模型的多尺度基函数，并采用超样本技术进一步提高计算准确性.数值结果表明，多尺度有限元算法与机器学习结合是一种有效的流体流动模拟算法.

A Multiscale Deep Learning Model for Fractured Porous Media

A multiscale deep learning model is proposed for fluid flow in porous media. The method is formulated on hierarchical grid system, that is, a coarse grid and a fine grid. Deep learning network is used to train data on the coarse gird. Source term and permeability field is treated as input parameter and coarse-scale solution is treated as output parameter. We construct multiscale basis functions by solving local flow problems within coarse gridcells. Heterogeneity and interactions between matrix and fracture are captured by basis functions. Oversampling technique is applied to get more accurate small-scale details. Numerical experiments show that the multiscale deep learning model is promising for flow simulation in heterogeneous and fractured porous media.