Advancing fluid dynamics simulations: A comprehensive approach to optimizing physics-informed neural networks Article Swipe
Wen Zhou
,
Shuichiro Miwa
,
Koji Okamoto
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1063/5.0180770
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1063/5.0180770
Flow modeling based on physics-informed neural networks (PINNs) is emerging as a potential artificial intelligence (AI) technique for solving fluid dynamics problems. However, conventional PINNs encounter inherent limitations when simulating incompressible fluids, such as difficulties in selecting the sampling points, balancing the loss items, and optimizing the hyperparameters. These limitations often lead to non-convergence of PINNs. To overcome these issues, an improved and generic PINN for fluid dynamic analysis is proposed. This approach incorporates three key improvements: residual-based adaptive sampling, which automatically samples points in areas with larger residuals; adaptive loss weights, which balance the loss terms effectively; and utilization of the differential evolution optimization algorithm. Then, three case studies at low Reynolds number, Kovasznay flow, vortex shedding past a cylinder, and Beltrami flow are employed to validate the improved PINNs. The contribution of each improvement to the final simulation results is investigated and quantified. The simulation results demonstrate good agreement with both analytical solutions and benchmarked computational fluid dynamics (CFD) calculation results, showcasing the efficiency and validity of the improved PINNs. These PINNs have the potential to reduce the reliance on CFD simulations for solving fluid dynamics problems.
Related Topics
Concepts
Computational fluid dynamics
Fluid dynamics
Convergence (economics)
Adaptive sampling
Physics
Reynolds number
Artificial neural network
Vortex shedding
Flow (mathematics)
Sampling (signal processing)
Hyperparameter
Statistical physics
Applied mathematics
Computer science
Mechanics
Turbulence
Algorithm
Artificial intelligence
Mathematics
Statistics
Detector
Optics
Economics
Monte Carlo method
Economic growth
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1063/5.0180770
- https://pubs.aip.org/aip/pof/article-pdf/doi/10.1063/5.0180770/18573823/013615_1_5.0180770.pdf
- OA Status
- bronze
- Cited By
- 21
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390988447
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4390988447Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1063/5.0180770Digital Object Identifier
- Title
-
Advancing fluid dynamics simulations: A comprehensive approach to optimizing physics-informed neural networksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-01Full publication date if available
- Authors
-
Wen Zhou, Shuichiro Miwa, Koji OkamotoList of authors in order
- Landing page
-
https://doi.org/10.1063/5.0180770Publisher landing page
- PDF URL
-
https://pubs.aip.org/aip/pof/article-pdf/doi/10.1063/5.0180770/18573823/013615_1_5.0180770.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
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bronzeOpen access status per OpenAlex
- OA URL
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https://pubs.aip.org/aip/pof/article-pdf/doi/10.1063/5.0180770/18573823/013615_1_5.0180770.pdfDirect OA link when available
- Concepts
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Computational fluid dynamics, Fluid dynamics, Convergence (economics), Adaptive sampling, Physics, Reynolds number, Artificial neural network, Vortex shedding, Flow (mathematics), Sampling (signal processing), Hyperparameter, Statistical physics, Applied mathematics, Computer science, Mechanics, Turbulence, Algorithm, Artificial intelligence, Mathematics, Statistics, Detector, Optics, Economics, Monte Carlo method, Economic growthTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
21Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 12, 2024: 9Per-year citation counts (last 5 years)
- References (count)
-
37Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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