Heterogeneous feature fusion based machine learning on shallow-wide and heterogeneous-sparse scientific dataset

A General Framework Combining Generative Adversarial Networks and Mixture Density Networks for Inverse Modeling in Microstructural Materials Design

Learning to Predict Crystal Plasticity at the Nanoscale:Deep Residual Networks and Size Effects in UniaxialCompression Discrete Dislocation Simulations

A Deep Learning Model For Localization of Two-Phase High-Contrast Three-dimensional Materials

Microstructural Materials Design Via Deep Adversarial Learning Methodology

A deep learning based domain knowledge integration for small dataset

PSED2

EECS495_machine_learning