The DOI matches its Crossref record; 120 of the 120 references it cites were found in a registry or an index.
Understanding Physics-Informed Neural Networks: Techniques, Applications, Trends, and Challenges
Open accessAbstract
Physics-informed neural networks (PINNs) represent a significant advancement at the intersection of machine learning and physical sciences, offering a powerful framework for solving complex problems governed by physical laws. This survey provides a comprehensive review of the current state of research on PINNs, highlighting their unique methodologies, applications, challenges, and future directions. We begin by introducing the fundamental concepts underlying neural networks and the motivation for integrating physics-based constraints. We then explore various PINN architectures and techniques for incorporating physical laws into neural network training, including approaches to solving partial differential equations (PDEs) and ordinary differential equations (ODEs). Additionally, we discuss the primary challenges faced in developing and applying PINNs, such as computational complexity, data scarcity, and the integration of complex physical laws. Finally, we identify promising future research directions. Overall, this survey seeks to provide a foundational understanding of PINNs within this rapidly evolving field.
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Cites
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Cited by
Du, Z., & Lu, R. (2025) · Batteries · doi:10.3390/batteries11060204 · Cited by 58
Dini, P., & Paolini, D. (2025) · Batteries · doi:10.3390/batteries11030107 · Cited by 40
(2025) · doi:10.1016/j.iot.2026.101938 · Cited by 31
Metadata
- Authors
- Farea, A., Yli-Harja, O., & Emmert-Streib, F.
- Venue
- AI
- Publisher
- MDPI AG
- Published
- 2024-08-29
- Volume
- 5
- Issue
- 3
- Pages
- 1534-1557
- ISSN
- 2673-2688 (ISSN-L)
- OpenAlex
- W4401992148
- Cited by
- 259
- References
- 120
- FWCI
- 38.52
- Type
- journal article
- Language
- en
- Open access
- gold
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