Discretizationnet: a machine-learning based solver for Navier-Stokes equations using finite volume discretization
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Publication:2021855
DOI10.1016/j.cma.2021.113722zbMath1506.76115arXiv2005.08357OpenAlexW3135391574MaRDI QIDQ2021855
Chris Hill, Jay Pathak, Rishikesh Ranade
Publication date: 27 April 2021
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2005.08357
Navier-Stokes equations for incompressible viscous fluids (76D05) Finite volume methods applied to problems in fluid mechanics (76M12) Finite volume methods for boundary value problems involving PDEs (65N08)
Related Items (12)
CAN-PINN: a fast physics-informed neural network based on coupled-automatic-numerical differentiation method ⋮ Learning finite element convergence with the multi-fidelity graph neural network ⋮ VPVnet: A Velocity-Pressure-Vorticity Neural Network Method for the Stokes’ Equations under Reduced Regularity ⋮ Machine Learning Surrogate Modeling for Meshless Methods: Leveraging Universal Approximation ⋮ A nonlocal energy‐informed neural network for isotropic elastic solids with cracks under thermomechanical loads ⋮ A physics-informed convolutional neural network for the simulation and prediction of two-phase Darcy flows in heterogeneous porous media ⋮ Solving seepage equation using physics-informed residual network without labeled data ⋮ A conservative hybrid deep learning method for Maxwell-Ampère-Nernst-Planck equations ⋮ QBoost for regression problems: solving partial differential equations ⋮ DiscretizationNet ⋮ PhyCRNet: physics-informed convolutional-recurrent network for solving spatiotemporal PDEs ⋮ Physics-informed graph neural Galerkin networks: a unified framework for solving PDE-governed forward and inverse problems
Uses Software
Cites Work
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