Physical properties prediction of heterogeneous materials using supervised learning

Physical Properties: Young’s Modulus, Diffusion Coefficient, Permeability
Material Samples: Sandstone porous materials with different volume fractions
Supervised Learning Algorithm:
Convoluted Nerual Network (CNN): Nine layers with six convolution layer and three fully connected layer
Residual Network (ResNew):
Regression: Linear Regression(RidgeCV), Support Vector Machine(SVR), Multi-layer Perceptron(MLP), K-Nearest Neighbours(KNN), Decision Tree, Random Forest, AdaBoost, Gradient Boost, Bagging