摘要
由新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-详细的机器学习数据已经呈现。根据NewsR X记者在佛罗里达州盖恩斯维尔的新闻报道，研究表明，“各向异性物理-正则化可解释机器学习微结构进化(APRIMME)是一种通用的晶粒生长模拟机器学习解决方案。在之前的工作中，prime使用了深度神经网络来预测特定地点的迁移，作为其nei ghboring站点的函数，以模拟正常的、各向同性的晶粒生长行为。”
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Machine Learning have been presented. According to news reporting from Gainesville, Florida, by NewsR x journalists, research stated, “Anisotropic Physics-Regularized Interpretable M achine Learning Microstructure Evolution (APRIMME) is a general-purpose machine learning solution for grain growth simulations. In prior work, PRIMME employed a deep neural network to predict site-specific migration as a function of its nei ghboring sites to model normal, isotropic, grain growth behavior.”
NETL
NSTL