most citedSolving the linear transport equation by a deep neural network approach

1 citations · 2 across the 3 of their papers we have counts for

collaborators

5 papers

math.NA20211 cited

Solving the linear transport equation by a deep neural network approach

Zheng Chen, Liu Liu, Lin Mu

In this paper, we study the linear transport model by adopting the deep learning method, in particular the deep neural network (DNN) approach. While the interest of using DNN to st…

math.NA2020

A Pressure-Robust Weak Galerkin Finite Element Method for Navier-Stokes Equations

Lin Mu

In this paper, we develop and analyze a novel numerical scheme for the steady incompressible Navier-Stokes equations by the weak Galerkin methods. The divergence-preserving velocit…

math.NA20201 cited

A stabilizer free, pressure robust, and superconvergence weak Galerkin finite element method for the Stokes Equations on polytopal mesh

Lin Mu, Xiu Ye, Shangyou Zhang

In this paper, we propose a new stabilizer free and pressure robust WG method for the Stokes equations with super-convergence on polytopal mesh in the primary velocity-pressure for…

math.OC2020

Optimal Control of Convection-Cooling and Numerical Implementation

Cuiyu He, Weiwei Hu, Lin Mu

This paper is concerned with the problem of enhancing convection-cooling via active control of the incompressible velocity field, described by a stationary diffusion-convection mod…

cs.MS2020

Accelerating linear solvers for Stokes problems with C++ metaprogramming

Denis Demidov, Lin Mu, Bin Wang

The efficient solution of large sparse saddle point systems is very important in computational fluid mechanics. The discontinuous Galerkin finite element methods have become increa…