Physics-Guided Deep Learning for Dynamical Systems: A Survey Article Swipe
Modeling complex physical dynamics is a fundamental task in science and engineering. Traditional physics-based models are sample efficient, and interpretable but often rely on rigid assumptions. Furthermore, direct numerical approximation is usually computationally intensive, requiring significant computational resources and expertise, and many real-world systems do not have fully-known governing laws. While deep learning (DL) provides novel alternatives for efficiently recognizing complex patterns and emulating nonlinear dynamics, its predictions do not necessarily obey the governing laws of physical systems, nor do they generalize well across different systems. Thus, the study of physics-guided DL emerged and has gained great progress. Physics-guided DL aims at taking the best from both physics-based modeling and state-of-the-art DL models to better solve scientific problems. In this article, we provide a structured overview of existing methodologies of integrating prior physical knowledge or physics-based modeling into DL, with a special emphasis on learning dynamical systems. We also discuss the fundamental challenges and emerging opportunities in the area.
Related Topics
Concepts
Physical system
Physical law
Task (project management)
Complex system
Computer science
Physical science
Dynamical systems theory
Deep learning
Artificial intelligence
Nonlinear system
Management science
Data science
Systems engineering
Physics
Engineering
Mathematics
Mathematics education
Quantum mechanics
Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.1145/3766887
- OA Status
- hybrid
- Cited By
- 6
- References
- 169
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3181290273
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W3181290273Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1145/3766887Digital Object Identifier
- Title
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Physics-Guided Deep Learning for Dynamical Systems: A SurveyWork title
- Type
-
reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-09-25Full publication date if available
- Authors
-
Rui Wang, Rose YuList of authors in order
- Landing page
-
https://doi.org/10.1145/3766887Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1145/3766887Direct OA link when available
- Concepts
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Physical system, Physical law, Task (project management), Complex system, Computer science, Physical science, Dynamical systems theory, Deep learning, Artificial intelligence, Nonlinear system, Management science, Data science, Systems engineering, Physics, Engineering, Mathematics, Mathematics education, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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6Total citation count in OpenAlex
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2025: 2, 2024: 1, 2023: 2, 2021: 1Per-year citation counts (last 5 years)
- References (count)
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169Number of works referenced by this work
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20Other works algorithmically related by OpenAlex
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