Learning dynamical systems from data: An introduction to physics-guided deep learning Article Swipe
Modeling complex physical dynamics is a fundamental task in science and engineering. Traditional physics-based models are first-principled, explainable, and sample-efficient. However, they often rely on strong modeling assumptions and expensive numerical integration, requiring significant computational resources and domain expertise. While deep learning (DL) provides efficient alternatives for modeling complex dynamics, they require a large amount of labeled training data. Furthermore, its predictions may disobey the governing physical laws and are difficult to interpret. Physics-guided DL aims to integrate first-principled physical knowledge into data-driven methods. It has the best of both worlds and is well equipped to better solve scientific problems. Recently, this field has gained great progress and has drawn considerable interest across discipline Here, we introduce the framework of physics-guided DL with a special emphasis on learning dynamical systems. We describe the learning pipeline and categorize state-of-the-art methods under this framework. We also offer our perspectives on the open challenges and emerging opportunities.
Related Topics
Concepts
Computer science
Data science
Pipeline (software)
Field (mathematics)
Artificial intelligence
Deep learning
Dynamical systems theory
Physical system
Categorization
Task (project management)
Physical law
Domain (mathematical analysis)
Physical science
Management science
Systems engineering
Engineering
Physics
Mathematics education
Mathematics
Mathematical analysis
Programming language
Pure mathematics
Quantum mechanics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1073/pnas.2311808121
- https://www.pnas.org/doi/pdf/10.1073/pnas.2311808121
- OA Status
- hybrid
- Cited By
- 57
- References
- 118
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399940222
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4399940222Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1073/pnas.2311808121Digital Object Identifier
- Title
-
Learning dynamical systems from data: An introduction to physics-guided deep learningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-06-24Full publication date if available
- Authors
-
Rose Yu, Rui WangList of authors in order
- Landing page
-
https://doi.org/10.1073/pnas.2311808121Publisher landing page
- PDF URL
-
https://www.pnas.org/doi/pdf/10.1073/pnas.2311808121Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
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https://www.pnas.org/doi/pdf/10.1073/pnas.2311808121Direct OA link when available
- Concepts
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Computer science, Data science, Pipeline (software), Field (mathematics), Artificial intelligence, Deep learning, Dynamical systems theory, Physical system, Categorization, Task (project management), Physical law, Domain (mathematical analysis), Physical science, Management science, Systems engineering, Engineering, Physics, Mathematics education, Mathematics, Mathematical analysis, Programming language, Pure mathematics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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57Total citation count in OpenAlex
- Citations by year (recent)
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2025: 50, 2024: 6, 2023: 1Per-year citation counts (last 5 years)
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118Number of works referenced by this work
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-
10Other works algorithmically related by OpenAlex
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