A comprehensive review of advances in physics-informed neural networks and their applications in complex fluid dynamics
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Abstract
Physics-informed neural networks (PINNs) represent an emerging computational paradigm that incorporates observed data patterns and the fundamental physical laws of a given problem domain. This approach provides significant advantages in addressing diverse difficulties in the field of complex fluid dynamics. We thoroughly investigated the design of the model architecture, the optimization of the convergence rate, and the development of computational modules for PINNs. However, efficiently and accurately utilizing PINNs to resolve complex fluid dynamics problems remain an enormous barrier. For instance, rapidly deriving surrogate models for turbulence from known data and accurately characterizing flow details in…
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5Topics & keywords
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Keywords
- Physics
- Fluid dynamics
- Statistical physics
- Artificial neural network
- Dynamics (music)
- Management science
- Artificial intelligence
- Mechanics
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