Guest Editorial: Scientific and Physics-Informed Machine Learning for Industrial Applications
Francesco Piccialli, Fabio Giampaolo, David Camacho, Gang Mei · IEEE Transactions on Industrial Informatics · 2022
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.