Semantic Scholar Open Access 2022 182 sitasi

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

Chuizheng Meng Sungyong Seo Defu Cao Sam Griesemer Yan Liu

Lihat Sumber DOI

Abstrak

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.

Topik & Kata Kunci

Computer Science Mathematics

Penulis (5)

Chuizheng Meng

Sungyong Seo

Defu Cao

Sam Griesemer

Yan Liu

Format Sitasi

APA MLA BibTeX

Meng, C., Seo, S., Cao, D., Griesemer, S., Liu, Y. (2022). When physics meets machine learning: a survey of physics-informed machine learning. https://doi.org/10.1007/s44379-025-00016-0

Akses Cepat

Lihat di Sumber doi.org/10.1007/s44379-025-00016-0

Informasi Jurnal

Tahun Terbit: 2022
Bahasa: en
Total Sitasi: 182×
Sumber Database: Semantic Scholar
DOI: 10.1007/s44379-025-00016-0
Akses: Open Access ✓