论文标题
无监督的物理概念的机器学习
Unsupervised machine learning for physical concepts
论文作者
论文摘要
近年来，机器学习方法已被用来帮助科学家进行科学研究。人类科学理论基于一系列概念。机器如何从实验数据中学习概念将是重要的第一步。我们提出了一种混合方法，通过无监督的机器学习提取可解释的物理概念。该方法由两个阶段组成。首先，我们需要找到实验数据的贝蒂数量。其次，考虑到Betti数字，我们使用变异自动编码器网络来提取有意义的物理变量。我们在玩具模型上测试我们的协议，并显示其工作原理。
In recent years, machine learning methods have been used to assist scientists in scientific research. Human scientific theories are based on a series of concepts. How machine learns the concepts from experimental data will be an important first step. We propose a hybrid method to extract interpretable physical concepts through unsupervised machine learning. This method consists of two stages. At first, we need to find the Betti numbers of experimental data. Secondly, given the Betti numbers, we use a variational autoencoder network to extract meaningful physical variables. We test our protocol on toy models and show how it works.
