A Novel Physics-Regularized Interpretable Machine Learning Model for Grain Growth
Abstract
Experimental grain growth observations often deviate from grain growth simulations, revealing that the governing rules for grain boundary motion are not fully understood. A novel deep learning model was developed to capture grain growth behavior from training data without making assumptions about the underlying physics. The Physics-Regularized Interpretable Machine Learning Microstructure Evolution (PRIMME) model consists of a multi-layer neural network that predicts the likelihood of a point changing to a neighboring grain. Here, we demonstrate PRIMME's ability to replicate two-dimensional normal grain growth by training it with Monte Carlo Potts simulations. The trained PRIMME model's grain growth predictions in several test cases show good agreement with analytical models, phase-field simulations, Monte Carlo Potts simulations, and results from the literature. Additionally, PRIMME's adaptability to investigate irregular grain growth behavior is shown. Important aspects of PRIMME like interpretability, regularization, extrapolation, and overfitting are also discussed.
Cite
@article{arxiv.2203.03735,
title = {A Novel Physics-Regularized Interpretable Machine Learning Model for Grain Growth},
author = {Weishi Yan and Joseph Melville and Vishal Yadav and Kristien Everett and Lin Yang and Michael S. Kesler and Amanda R. Krause and Michael R. Tonks and Joel B. Harley},
journal= {arXiv preprint arXiv:2203.03735},
year = {2022}
}
Comments
31 pages, 12 figures. Accepted to Materials & Design. Code Available: https://github.com/EAGG-UF/PRIMME