@inproceedings{cc602d985e6142d5b7eb063588fe4cb0,
title = "A Poisson's Equation Solver Based on Neural Network Precondtioned CG Method",
abstract = "In this study, we investigate the feasibility of utilizing deep learning technique to construct preconditioners for iterative matrix solvers. A neural network (NN) is proposed to simulate the optimum-preconditioner's mapping properties, and participates in NN-precondtioned conjugate gradient (NNPCG) method. Training and testing sets are generated by finite difference method (FDM). Numerical examples demonstrate that compared to conjugate gradient (CG) method, NN-PCG significantly improves convergence performance on solving 2-D Possion's equation.",
keywords = "conjugate gradient method, deep learning, neural network, preconditioner",
author = "Tianchen Shao and Tao Shan and Maokun Li and Fan Yang and Shenheng Xu",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022 ; Conference date: 09-12-2022 Through 12-12-2022",
year = "2022",
doi = "10.1109/ACES-China56081.2022.10064917",
language = "英语",
series = "2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022",
address = "美国",
}