Cover Image

A model-data asymptotic-preserving neural network method based on micro-macro decomposition for gray radiative transfer equations

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Li, Hongyan, Jiang, Song, Sun, Wenjun, Xu, Liwei, Zhou, Guanyu
Format:	Preprint
Published:	2022
Subjects:	Numerical Analysis Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Macroscopic auxiliary asymptotic preserving neural networks for the linear radiative transfer equations
by: Li, Hongyan, et al.
Published: (2024)

An asymptotic-preserving IMEX PN method for the gray model of the radiative transfer equation
by: Fu, Jinxue, et al.
Published: (2024)

An asymptotic-preserving method for the three-temperature radiative transfer model
by: Li, Ruo, et al.
Published: (2024)

An efficient asymptotic preserving Monte Carlo method for frequency-dependent radiative transfer equations
by: Hong, Yiyang, et al.
Published: (2025)

A Bi-fidelity based asymptotic-preserving neural network for the semiconductor Boltzmann equation and its inverse problem
by: Liu, Liu, et al.
Published: (2025)

Uniformly accurate structure-preserving neural surrogates for radiative transfer
by: Bai, Mengjia, et al.
Published: (2025)

Subspace method based on neural networks for solving the partial differential equation
by: Xu, Zhaodong, et al.
Published: (2024)

Data-driven optimized high-order WENO schemes with low-dissipation and low-dispersion
by: Zhou, Jinrui, et al.
Published: (2025)

Learning high-accuracy numerical schemes for hyperbolic equations on coarse meshes
by: Zhou, Jinrui, et al.
Published: (2025)

Subspace method based on neural networks for solving the partial differential equation in weak form
by: Liu, Pengyuan, et al.
Published: (2024)

A GPU-accelerated Cartesian grid method is proposed for solving the heat, wave, and Schrodinger equations on irregular domains
by: Tan, Liwei, et al.
Published: (2024)

Structure and asymptotic preserving deep neural surrogates for uncertainty quantification in multiscale kinetic equations
by: Chen, Wei, et al.
Published: (2025)

Improved randomized neural network methods with boundary processing for solving elliptic equations
by: Zhou, Huifang, et al.
Published: (2024)

A GPU-accelerated Cartesian grid method for PDEs on irregular domain
by: Tan, Liwei, et al.
Published: (2024)

A quantitative comparison of high-order asymptotic-preserving and asymptotically-accurate IMEX methods for the Euler equations with non-ideal gases
by: Orlando, Giuseppe, et al.
Published: (2025)

Error analysis of an asymptotic-preserving, energy-stable finite volume method for barotropic Euler equations
by: Anandan, Megala, et al.
Published: (2026)

Error estimates of asymptotic-preserving neural networks in approximating stochastic linearized Boltzmann equation
by: Wan, Jiayu, et al.
Published: (2025)

Multilevel domain decomposition-based architectures for physics-informed neural networks
by: Dolean, Victorita, et al.
Published: (2023)

Robust and conservative dynamical low-rank methods for the Vlasov equation via a novel macro-micro decomposition
by: Coughlin, Jack, et al.
Published: (2023)

Adaptive neural network basis methods for partial differential equations with low-regular solutions
by: Huang, Jianguo, et al.
Published: (2024)

Domain decomposition dynamical low-rank for multi-dimensional radiative transfer equations
by: Brunner, Stefan, et al.
Published: (2026)

A hybridizable discontinuous Galerkin method for the indefinite time-harmonic Maxwell equations
by: Chen, Gang, et al.
Published: (2019)

Symmetry group based domain decomposition to enhance physics-informed neural networks for solving partial differential equations
by: Liu, Ye, et al.
Published: (2024)

An asymptotic preserving scheme for the quantum Liouville-BGK equation
by: Duboscq, Romain, et al.
Published: (2025)

Provably realizability-preserving finite volume method for quadrature-based moment models of kinetic equations
by: Fan, Chuan, et al.
Published: (2025)

A discrete-ordinate weak Galerkin method for radiative transfer equation
by: Singh, Maneesh Kumar
Published: (2022)

Structure-preserving numerical method for Maxwell-Ampère Nernst-Planck model
by: Qiao, Zhonghua, et al.
Published: (2022)

Physics informed neural network for forward and inverse radiation heat transfer in graded-index medium
by: Murari, K., et al.
Published: (2024)

Structure-preserving nodal DG method for the Euler equations with gravity: well-balanced, entropy stable, and positivity preserving
by: Liu, Yuchang, et al.
Published: (2025)

An asymptotic-preserving active flux scheme for the hyperbolic heat equation in the diffusive scaling
by: Duan, Junming, et al.
Published: (2025)

A discontinuous plane wave neural network method for Helmholtz equation and time-harmonic Maxwell's equations
by: Yuan, Long, et al.
Published: (2023)

A matrix preconditioning framework for physics-informed neural networks based on adjoint method
by: Song, Jiahao, et al.
Published: (2025)

Realizability-preserving monolithic convex limiting in continuous Galerkin discretizations of the M1 model of radiative transfer
by: Moujaes, Paul, et al.
Published: (2025)

A note on the adjoint method for neural ordinary differential equation network
by: Hu, Pipi
Published: (2024)

Reduced order models for nonlinear radiative transfer based on moment equations and POD/DMD of Eddington tensor
by: Coale, Joseph M., et al.
Published: (2021)

Weak TransNet: A Petrov-Galerkin based neural network method for solving elliptic PDEs
by: Xu, Zhihang, et al.
Published: (2025)

Structure-preserving neural networks for the regularized entropy-based closure of the Boltzmann moment system
by: Schotthöfer, Steffen, et al.
Published: (2024)

High-order structure-preserving schemes for the regularized logarithmic Schrödinger equation
by: Yang, Fan, et al.
Published: (2024)

A novel number-theoretic sampling method for neural network solutions of partial differential equations
by: Yang, Yu, et al.
Published: (2024)

A structure and asymptotic preserving scheme for the Vlasov-Poisson-Fokker-Planck model
by: Blaustein, Alain, et al.
Published: (2023)