Scientific Machine Learning: Principles, Methods and Applications

A recent report by the US Department of Energy defines the area of scientific machine learning as “a core component of artificial intelligence (AI) and a computational technology that can be trained, with scientific data, to augment or automate human skills”, which has “the potential to transform science and energy research”, through “investments in massive data from scientific user facilities, software for predictive models and algorithms, high-performance computing platforms, and the national workforce”. Significant new results have been reported in the emerging area of physics-informed neural networks, which is aimed at developing neural network architectures for the solution of partial differential equations and inverse problems. In this presentation, we give an overview of this area and its most promising applications for microwave engineering.