论文标题
地下流量模拟的替代建模的卷积神经网络
Recurrent convolutional neural network for the surrogate modeling of subsurface flow simulation
论文作者
论文摘要
多尺度的异质性和位点表征不足，通常会阻碍多孔介质流体流量的不确定性定量。蒙特卡罗模拟（MCS）运行数值模拟以实现大量输入参数时，当模拟成本昂贵或不确定性程度很大时，就变得不可行。为了替代数值流量模拟，开发了许多基于深神经网络的方法，但是以前的研究仅着重于在固定时间步骤下生成几个输出的快照，并且缺乏反映模拟数据的时间依赖性属性。最近，卷积长期内存（ConvlstM）用于处理时间序列图像数据。在这里，我们建议将SEGNET与Convlstm层相结合，以进行数值流量模拟的替代建模。结果表明，当仿真的输出为时间序列数据时，提出的方法可以显着提高基于SEGNET的替代模型的性能。
The quantification of uncertainty on fluid flow in porous media is often hampered by multi-scale heterogeneity and insufficient site characterization. Monte-Carlo simulation (MCS), which runs numerical simulations for a large number of realization of input parameters , becomes infeasible when simulation cost is expensive or the degree of uncertainty is large. Many deep-neural-network-based methods are developed in order to replace the numerical flow simulation, but previous studies focused only on generating several snapshots of outputs at the fixed time steps, and lack to reflect the time dependent property of simulation data. Recently, the convolutional long short term memory (ConvLSTM) is utilized to deal with time series image data. Here, we propose to combine SegNet with ConvLSTM layers for the surrogate modeling of numerical flow simulation. The results show that the proposed method improves the performance of SegNet based surrogate model remarkably when the output of the simulation is time series data.
