“When physics meets machine learning: a survey of physics-informed machine learning” (2025), by Chuizheng Meng et al. Physics and Astronomy research paper. Original abstract below, unchanged. A free PDF is available.

When physics meets machine learning: a survey of physics-informed machine learning

Chuizheng Meng, Sam Griesemer, Defu Cao et al. · May 7, 2025 · Physics and Astronomy

Abstract

Physics-informed machine learning (PIML), the combination of prior physics knowledge with data-driven machine learning models, has emerged as an effective means of mitigating a shortage of training data, increasing model generalizability, and ensuring physical plausibility of results. In this paper, we survey a wide variety of recent works in PIML and summarize them from three key aspects: 1) motivations of PIML, 2) physics knowledge in PIML, and 3) methods of physics knowledge integration in PIML. We additionally discuss current challenges and corresponding research opportunities in PIML.

Free PDF DOI: 10.1007/s44379-025-00016-0