Physics-Informed Machine Learning for Two-Phase Moving-Interface and Stefan Problems

This article likely discusses the application of physics-informed machine learning (PIML) to solve problems involving moving interfaces, such as those found in two-phase flow or phase change phenomena (Stefan problems). The use of PIML suggests an attempt to incorporate physical laws and constraints into the machine learning model, potentially improving accuracy and efficiency compared to purely data-driven approaches. The source, ArXiv, indicates this is a pre-print or research paper.