Transferable 3D Convolutional Neural Networks for Elastic Constants Prediction in Nanoporous Metals
This study demonstrates that transferable 3D Convolutional Neural Networks, specifically the DenseNet-201 architecture, significantly outperform traditional descriptor-based models in predicting the elastic constants of nanoporous metals, achieving high accuracy () and enabling the identification of Pareto optimal designs through transfer learning and large-scale stochastic evaluation.