论文标题
域知识辅助机器学习方法用于软磁金属眼镜的性能预测
Domain-knowledge-aided machine learning method for properties prediction of soft magnetic metallic glasses
论文作者
论文摘要
提出了通过域知识帮助的机器学习（ML）方法来预测软磁金属玻璃（MGS）的饱和磁化（BS）和临界直径（DMAX）。基于发布的有关软磁MGS的已发表的实验作品建立了两个数据集。提出了一般特征空间，并被证明是针对不同预测任务的ML模型培训的自适应。发现ML模型的预测性能比传统的基于物理知识的估计方法更好。此外，域知识辅助特征选择可以大大减少特征的数量，而不会显着降低预测准确性。最后，研究了软磁金属玻璃的临界大小的二进制分类。
A machine learning (ML) method aided by domain knowledge was proposed to predict saturated magnetization (Bs) and critical diameter (Dmax) of soft magnetic metallic glass (MGs). Two datasets were established based on published experimental works about soft magnetic MGs. A general feature space was proposed and proved to be adaptive for ML model training for different prediction tasks. It was found that the predictive performance of ML models was better than traditional physical knowledge-based estimation methods. In addition, domain knowledge aided feature selection can greatly reduce the number of features without significantly reducing the prediction accuracy. Finally, binary classification of the critical size of soft magnetic metallic glass was studied.
