Abstract：In this work, a convolutional neural network was utilized to train and predict the effective diffusivity of porous material. The microstructure of porous material was generated by python script and the effective diffusivity was calculated by finite element method and using Matlab and Comsol. The training of convolutional neural network was carried on a NVIDIA K80 GPU of Google Co-laboratory, and Dropout method was utilized to reduce the over-fitting during the training process. Finally, the prediction accuracy of the trained convolutional neural network on the test samples reaches as high as 96.70%. This method can effectively and precisely predicts the effective diffusivity of porous material by using its graph information such as scanning electron microscope (SEM) pictures.
Song Xinkuan,Ye Guanghua,Zhou Jinghong et al. Prediction of Effective Diffusivity of Porous Material Based on Convolutional Neural Network[J]. 化学反应工程与工艺, 2018, 34(2): 97-103.
