论文标题
机器学习空间的分类
Machine-Learning the Classification of Spacetimes
论文作者
论文摘要
关于悠久的分类问题，我们通过采用机器学习和现代数据科学的富有成果的技术来采取新颖的观点。特别是，我们对彼得罗夫（Petrov）的空间分类进行了建模，并表明喂养前馈神经网络可以取得高度的成功。我们还展示了如何减少维数的数据可视化技术可以帮助分析不同类型的空间结构中的基本模式。
On the long-established classification problems in general relativity we take a novel perspective by adopting fruitful techniques from machine learning and modern data-science. In particular, we model Petrov's classification of spacetimes, and show that a feed-forward neural network can achieve high degree of success. We also show how data visualization techniques with dimensionality reduction can help analyze the underlying patterns in the structure of the different types of spacetimes.
