Guest Editorial: Special Issue on Physics-Informed Machine Learning

Abstract

The special issue delves into the tantalizing prospects of machine learning for multiscale modeling, a domain where the traditional methodologies often encounter scalability issues. Here, Physics-informed machine learning (PIML) promises to bridge scales from the microscopic to the macroscopic, creating models that are not only scalable but also more accurate and less resource-intensive. Furthermore, the contributors have taken on the challenge of machine learning model interpretability. They have explored how these models can provide insights into physical systems, thus serving a dual purpose of solving complex problems while also contributing to the body of knowledge in physics. The integration of physical laws with machine learning is not just an innovation; it is a renaissance of understanding. The papers in this issue showcase the pioneering works that merge the robustness of physics with the flexibility of machine learning. Here, we provide an overview of the significant contributions made by our authors in advancing the field of PIML.