2 citations · 6 across the 7 of their papers we have counts for
8 papers
Energy Approach from -Graph to Continuum Diffusion Model with Connectivity Functional
Yahong Yang, Sun Lee, Jeff Calder +1
We derive an energy-based continuum limit for -graphs endowed with a general connectivity functional. We prove that the discrete energy and its continuum counterpart d…
Learn Singularly Perturbed Solutions via Homotopy Dynamics
Chuqi Chen, Yahong Yang, Yang Xiang +1
Solving partial differential equations (PDEs) using neural networks has become a central focus in scientific machine learning. Training neural networks for singularly perturbed pro…
An Imbalanced Learning-based Sampling Method for Physics-informed Neural Networks
Jiaqi Luo, Yahong Yang, Yuan Yuan +2
This paper introduces Residual-based Smote (RSmote), an innovative local adaptive sampling technique tailored to improve the performance of Physics-Informed Neural Networks (PINNs)…
Multiscale Neural Networks for Approximating Green's Functions
Wenrui Hao, Rui Peng Li, Yuanzhe Xi +2
Neural networks (NNs) have been widely used to solve partial differential equations (PDEs) in the applications of physics, biology, and engineering. One effective approach for solv…
On pattern formation in the thermodynamically-consistent variational Gray-Scott model
Wenrui Hao, Chun Liu, Yiwei Wang +1
In this paper, we explore pattern formation in a four-species variational Gary-Scott model, which includes all reverse reactions and introduces a virtual species to describe the bi…
Newton Informed Neural Operator for Computing Multiple Solutions of Nonlinear Partials Differential Equations
Wenrui Hao, Xinliang Liu, Yahong Yang
Solving nonlinear partial differential equations (PDEs) with multiple solutions using neural networks has found widespread applications in various fields such as physics, biology,…
Automatic Differentiation is Essential in Training Neural Networks for Solving Differential Equations
Chuqi Chen, Yahong Yang, Yang Xiang +1
Neural network-based approaches have recently shown significant promise in solving partial differential equations (PDEs) in science and engineering, especially in scenarios featuri…
A randomized Newton's method for solving differential equations based on the neural network discretization
Qipin Chen, Wenrui Hao
We develop a randomized Newton's method for solving differential equations, based on a fully connected neural network discretization. In particular, the randomized Newton's method…