On the locality of local neural operator in learning fluid dynamics
DOI10.1016/J.CMA.2024.117035MaRDI QIDQ6557796
Ximeng Ye, Jingjie Huang, Hong-Yu Li, Guoliang Qin
Publication date: 18 June 2024
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
computational fluid dynamicsreceptive fielddeep learningrange of dependencelocal neural operator (LNO)
Probabilistic models, generic numerical methods in probability and statistics (65C20) Gas dynamics (general theory) (76N15) Numerical methods for inverse problems for initial value and initial-boundary value problems involving PDEs (65M32)
Cites Work
- Title not available (Why is that?)
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- Time dependent boundary conditions for hyperbolic systems
- Finite element computation of unsteady viscous compressible flows
- \textit{hp}-VPINNs: variational physics-informed neural networks with domain decomposition
- PhyCRNet: physics-informed convolutional-recurrent network for solving spatiotemporal PDEs
- CAN-PINN: a fast physics-informed neural network based on coupled-automatic-numerical differentiation method
- Improved architectures and training algorithms for deep operator networks
- Conservative physics-informed neural networks on discrete domains for conservation laws: applications to forward and inverse problems
- ConvPDE-UQ: convolutional neural networks with quantified uncertainty for heterogeneous elliptic partial differential equations on varied domains
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- An overview of projection methods for incompressible flows
- Stochastic projection based approach for gradient free physics informed learning
- Spectral Methods
- Nonlinear dynamics and pattern formation in turbulent wake transition
- Solving high-dimensional partial differential equations using deep learning
- Prediction of turbulent heat transfer using convolutional neural networks
- Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
- Learning data-driven discretizations for partial differential equations
- Data-driven prediction of unsteady flow over a circular cylinder using deep learning
- Fekete-Gauss Spectral Elements for Incompressible Navier-Stokes Flows: The Two-Dimensional Case
- Novel DeepONet architecture to predict stresses in elastoplastic structures with variable complex geometries and loads
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