activity
20192022
most citedOn the Activation Function Dependence of the Spectral Bias of Neural Networks

9 citations · 9 across the 2 of their papers we have counts for

collaborators

5 papers

cs.LG20229 cited

On the Activation Function Dependence of the Spectral Bias of Neural Networks

Qingguo Hong, Jonathan W. Siegel, Qinyang Tan +1

Neural networks are universal function approximators which are known to generalize well despite being dramatically overparameterized. We study this phenomenon from the point of vie…

math.NA2020

An Abstract Stabilization Method with Applications to Nonlinear Incompressible Elasticity

Qingguo Hong, Chunmei Liu, Jinchao Xu

In this paper, we propose and analyze an abstract stabilized mixed finite element framework that can be applied to nonlinear incompressible elasticity problems. In the abstract sta…

math.NA2020

Robust block preconditioners for poroelasticity

Shuangshuang Chen, Qingguo Hong, Jinchao Xu +1

In this paper we study the linear systems arising from discretized poroelasticity problems. We formulate one block preconditioner for the two-filed Biot model and several precondit…

math.NA2019

Parameter-robust Uzawa-type iterative methods for double saddle point problems arising in Biot's consolidation and multiple-network poroelasticity models

Qingguo Hong, Johannes Kraus, Maria Lymbery +1

This work is concerned with the iterative solution of systems of quasi-static multiple-network poroelasticity (MPET) equations describing flow in elastic porous media that is perme…

math.NA2019

An Extended Galerkin Analysis for Elliptic Problems

Qingguo Hong, Shuonan Wu, Jinchao Xu

A general analysis framework is presented in this paper for many different types of finite element methods (including various discontinuous Galerkin methods). For second order elli…