Speaker
Rafał Topolnicki
Description
The talk will present results on predicting the elastic properties of metallic 3D porous materials using convolutional networks and additional structural descriptors. Even for relatively small datasets, the developed machine learning models achieve a prediction accuracy of Young’s modulus at R² ≥ 0.95 across a wide range of porous structures. The reference Young’s modulus values were obtained using molecular dynamics simulations and the finite element method. We will show that the approach is robust to distinct topologies of the porous media. The impact of adding topological summaries to the machine-learning model will be discussed.
