Guest Editorial: Scientific and Physics-Informed Machine Learning for Industrial Applications

Deep learning technology has become one of the core driving forces to promote the in-depth development of industrial automation. In [A1], Wang et al. interpreted the decision process of the convolutional neural network (CNN) by constructing a percolation model from a statistical physics perspective. In this perspective, the decision-making basis of CNN is difficult to understand, because CNN is usually used as a black box model. Furthermore, a novel concept of the differentiation degree and summarized an empirical formula for quantifying the differentiation degree is presented and discussed.

Automated summary

Deep learning technology is driving the in-depth development of industrial automation. Wang et al. interpret the decision process of convolutional neural networks (CNNs) using a percolation model from a statistical physics perspective. They introduce the concept of differentiation degree and present an empirical formula for quantifying it.