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

“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.

- Authors: Chuizheng Meng, Sam Griesemer, Defu Cao, Sungyong Seo, Yan Liu
- Published: May 7, 2025
- Field: Physics and Astronomy
- DOI: https://doi.org/10.1007/s44379-025-00016-0
- Free PDF: https://link.springer.com/content/pdf/10.1007/s44379-025-00016-0.pdf

## 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.

Canonical page: https://nerdi.pages.dev/paper/W4410165966
Read three real papers a day in Nerdi (iPhone): https://apps.apple.com/app/apple-store/id6813826387?pt=128757794&ct=web-paper&mt=8