Data-driven multi-grid solver for accelerated pressure projection
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Publication:2084099
DOI10.1016/J.COMPFLUID.2022.105620OpenAlexW3207720689MaRDI QIDQ2084099
Publication date: 17 October 2022
Published in: Computers and Fluids (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2110.11029
Uses Software
Cites Work
- Comparison of the lattice Boltzmann method and the artificial compressibility method for Navier-Stokes equations
- Accurate Cartesian-grid simulations of near-body flows at intermediate Reynolds numbers
- SPH for incompressible free-surface flows. Part I: Error analysis of the basic assumptions
- Deep learning of the spanwise-averaged Navier-Stokes equations
- Immersed boundary simulations of flows driven by moving thin membranes
- Black-box learning of multigrid parameters
- Prediction of aerodynamic flow fields using convolutional neural networks
- An angular momentum conserving affine-particle-in-cell method
- Julia: A Fresh Approach to Numerical Computing
- A Multigrid Tutorial, Second Edition
- Sparse Approximate Inverse Smoother for Multigrid
- Subgrid modelling for two-dimensional turbulence using neural networks
- Data Assimilation
- Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
- Adaptive Algebraic Multigrid
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