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20202026
most citedA comparison study of deep Galerkin method and deep Ritz method for elliptic problems with different boundary conditions

35 citations · 45 across the 7 of their papers we have counts for

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8 papers · 1 filter

math.NA2025

Uniformly accurate structure-preserving neural surrogates for radiative transfer

Mengjia Bai, Jingrun Chen, Keke Wu

In this work, we propose a uniformly accurate, structure-preserving neural surrogate for the radiative transfer equation with periodic boundary conditions based on a multiscale par…

math.NA2025

A Hybrid Discontinuous Galerkin Neural Network Method for Solving Hyperbolic Conservation Laws with Temporal Progressive Learning

Yan Shen, Jingrun Chen, Keke Wu

For hyperbolic conservation laws, traditional methods and physics-informed neural networks (PINNs) often encounter difficulties in capturing sharp discontinuities and maintaining t…

math.NA2025

Asymptotic-Preserving Neural Networks based on Even-odd Decomposition for Multiscale Gray Radiative Transfer Equations

Keke Wu, Xizhe Xie, Wengu Chen +2

We present a novel Asymptotic-Preserving Neural Network (APNN) approach utilizing even-odd decomposition to tackle the nonlinear gray radiative transfer equations (GRTEs). Our AP l…

math.NA2024

A Micro-Macro Decomposition-Based Asymptotic-Preserving Random Feature Method for Multiscale Radiative Transfer Equations

Jingrun Chen, Zheng Ma, Keke Wu

This paper introduces the Asymptotic-Preserving Random Feature Method (APRFM) for the efficient resolution of multiscale radiative transfer equations. The APRFM effectively address…

math.NA2023

An Unsupervised Deep Learning Approach for the Wave Equation Inverse Problem

Xiong-Bin Yan, Keke Wu, Zhi-Qin John Xu +1

Full-waveform inversion (FWI) is a powerful geophysical imaging technique that infers high-resolution subsurface physical parameters by solving a non-convex optimization problem. H…

math.NA2023

Asymptotic-Preserving Neural Networks for Multiscale Kinetic Equations

Shi Jin, Zheng Ma, Keke Wu

In this paper, we present two novel Asymptotic-Preserving Neural Networks (APNNs) for tackling multiscale time-dependent kinetic problems, encompassing the linear transport equatio…