|About this Abstract
||7th World Congress on Integrated Computational Materials Engineering (ICME 2023)
||Understanding Grain Growth Using a Physics-regularized Interpretable Machine Learning Model
||Joseph Melville, Vishal Yadav, Michael R. Tonks, Amanda Krause, Joel Harley
|On-Site Speaker (Planned)
||Michael R. Tonks
Physics-based mesoscale models of grain growth have been unable to accurately represent the grain growth behavior of real materials. We are developing the Physics Regularized Interpretable Machine Learning Microstructure Evolution (PRIMME) model that can learn to predict grain growth directly from experimental data. The PRIMME algorithm uses a multi-level neural network to predict grain growth in a voxelated domain. It uses a regularization function that encourages evolution that decreases the number of nearest neighbor voxels assigned to different grains. PRIMME helps to interpret and understand its learned grain growth behavior by determining the likelihood of a voxel changing to the grain of neighboring voxels. PRIMME was originally trained using data from 2D isotropic simulation results. It is now being extended to 3D isotropic and 2D anisotropic behavior. It will begin to be trained using experimental data rather than just simulation results in the near future.