Systematic Review of Physics Informedneural Networks on Solving Navies Stokes

DOI

10.2991/978-2-38476-462-4_105How to use a DOI?

Keywords

Physics informed neural network; navier-stokes equation; fluid mechanics; practical design in PINN

Abstract

With the rapid development of deep learning, Physics-Informed Neural Networks (PINNs), as an emerging technical means, are changing the way we solve fluid mechanics problems. PINN achieves an organic fusion of data-driven and physical models by embedding physical equations into neural networks. This article reviews the application of PINN in the field of fluid mechanics, discussing its basic principles, technical details, experimental design, challenges faced, and future prospects.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xian Yang
PY  - 2025
DA  - 2025/09/12
TI  - Systematic Review of Physics Informedneural Networks on Solving Navies Stokes
BT  - Proceedings of the 2025 9th International Seminar on Education, Management and Social Sciences (ISEMSS 2025)
PB  - Atlantis Press
SP  - 919
EP  - 926
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-462-4_105
DO  - 10.2991/978-2-38476-462-4_105
ID  - Yang2025
ER  -