  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.
