Investigation of Low and High-Speed Fluid Dynamics Problems Using Physics-Informed Neural Network
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Publication:6146817
DOI10.1080/10618562.2023.2285330MaRDI QIDQ6146817
Unnamed Author, Unnamed Author, Unnamed Author
Publication date: 31 January 2024
Published in: International Journal of Computational Fluid Dynamics (Search for Journal in Brave)
Navier-Stokes equationscompressible Euler equationdomain extensionphysics-informed neural networkSod shock-tubeweighted physics-informed neural network
Basic methods in fluid mechanics (76Mxx) Artificial intelligence (68Txx) Partial differential equations of mathematical physics and other areas of application (35Qxx)
Cites Work
- Inferring solutions of differential equations using noisy multi-fidelity data
- B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data
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- Physics-informed neural networks for solving forward and inverse flow problems via the Boltzmann-BGK formulation
- Physics-informed neural networks for inverse problems in supersonic flows
- Physics-informed neural networks for high-speed flows
- Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
- Data-driven inverse modelling through neural network (deep learning) and computational heat transfer
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Feedback control for unsteady flow and its application to the stochastic Burgers equation
- A numerical study of water-wave modulation based on a higher-order nonlinear Schrödinger equation
- Large Sample Properties of Simulations Using Latin Hypercube Sampling
- Deep learning in fluid dynamics
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