most citedAdaptive neural network basis methods for partial differential equations with low-regular solutions

1 citations · 1 across the 3 of their papers we have counts for

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3 papers

math.NA2026

Decoupled Divergence-Free Neural Networks Basis Method for Incompressible Fluid Problems

Jinbao Cheng, Jianguo Huang, Haoqin Wang +1

We propose a decoupled divergence-free neural networks basis (Decoupled-DFNN) method for solving incompressible flow problems, including the Stokes and Navier-Stokes equations. To…

math.NA2024★ 1 cited

Adaptive neural network basis methods for partial differential equations with low-regular solutions

Jianguo Huang, Haohao Wu, Tao Zhou

This paper aims to devise an adaptive neural network basis method for numerically solving a second-order semilinear partial differential equation (PDE) with low-regular solutions i…

math.NA2024

Newton's method and its hybrid with machine learning for Navier-Stokes Darcy Models discretized by mixed element methods

Jianguo Huang, Hui Peng, Haohao Wu

This paper focuses on discussing Newton's method and its hybrid with machine learning for the steady state Navier-Stokes Darcy model discretized by mixed element methods. First, a…