论文标题
一种新型的物理规范化的可解释的机器学习模型，用于谷物生长
A Novel Physics-Regularized Interpretable Machine Learning Model for Grain Growth
论文作者
论文摘要
实验性谷物生长观测通常偏离谷物生长模拟，表明谷物边界运动的管理规则尚未完全了解。开发了一种新颖的深度学习模型，以从训练数据中捕获谷物的生长行为，而无需对基础物理学做出假设。物理规范化的可解释的机器学习微观结构演化（PIRME）模型由多层神经网络组成，该网络预测了一个点变为相邻谷物的可能性。在这里，我们证明了Primme通过使用Monte Carlo Potts模拟训练二维正常谷物生长的能力。在几个测试案例中，受过训练的PRIMME模型的谷物生长预测表明，与分析模型，相位模拟，蒙特卡洛·波茨模拟以及文献结果良好一致。此外，还显示了Primme研究不规则谷物生长行为的适应性。还讨论了引物的重要方面，例如可解释性，正则化，外推和过度拟合。
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.
