Physics-informed neural networks (P INNs): application categories, trends and impact
International Journal of Numerical Methods for Heat & Fluid Flow
ISSN: 0961-5539
Article publication date: 10 July 2024
Issue publication date: 2 September 2024
Abstract
Purpose
This study aims to explore the evolving field of physics-informed neural networks (PINNs) through an analysis of 996 records retrieved from the Web of Science (WoS) database from 2019 to 2022.
Design/methodology/approach
WoS database was analyzed for PINNs using an inhouse python code. The author’s collaborations, most contributing institutes, countries and journals were identified. The trends and application categories were also analyzed.
Findings
The papers were classified into seven key domains: Fluid Dynamics and computational fluid dynamics (CFD); Mechanics and Material Science; Electromagnetism and Wave Propagation; Biomedical Engineering and Biophysics; Quantum Mechanics and Physics; Renewable Energy and Power Systems; and Astrophysics and Cosmology. Fluid Dynamics and CFD emerged as the primary focus, accounting for 69.3% of total publications and witnessing exponential growth from 22 papers in 2019 to 366 in 2022. Mechanics and Material Science followed, with an impressive growth trajectory from 3 to 65 papers within the same period. The study also underscored the rising interest in PINNs across diverse fields such as Biomedical Engineering and Biophysics, and Renewable Energy and Power Systems. Furthermore, the focus of the most active countries within each application category was examined, revealing, for instance, the USA’s significant contribution to Fluid Dynamics and CFD with 319 papers and to Mechanics and Material Science with 66 papers.
Originality/value
This analysis illuminates the rapidly expanding role of PINNs in tackling complex scientific problems and highlights its potential for future research across diverse domains.
Keywords
Acknowledgements
The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through large Research Group Project under the grant number (RGP2/194/45). This research of Mohammad Ghalambaz and Mikhail Sheremet was supported by the Tomsk State University Development Programme (Priority-2030).
Citation
Ghalambaz, M., Sheremet, M.A., Khan, M.A., Raizah, Z. and Shafi, J. (2024), "Physics-informed neural networks (P INNs): application categories, trends and impact", International Journal of Numerical Methods for Heat & Fluid Flow, Vol. 34 No. 8, pp. 3131-3165. https://doi.org/10.1108/HFF-09-2023-0568
Publisher
:Emerald Publishing Limited
Copyright © 2024, Emerald Publishing Limited