When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning
It provides a comprehensive overview for researchers in computational sciences, but is incremental as a survey paper.
This paper surveys physics-informed machine learning (PIML), which combines physics knowledge with data-driven models to address issues like data scarcity and improve generalizability and physical plausibility, summarizing recent works from motivations, physics knowledge, and integration methods.
Physics-informed machine learning (PIML), referring to the combination of prior knowledge of physics, which is the high level abstraction of natural phenomenons and human behaviours in the long history, with data-driven machine learning models, has emerged as an effective way to mitigate the shortage of training data, to increase models' generalizability and to ensure the physical plausibility of results. In this paper, we survey an abundant number of recent works in PIML and summarize them from three aspects: (1) motivations of PIML, (2) physics knowledge in PIML, (3) methods of physics knowledge integration in PIML. We also discuss current challenges and corresponding research opportunities in PIML.