Deep Learning Accelerated GMRES Solution of Electromagnetic Scattering From Dielectric Objects

Ji Yuan Wang*, Bo Wen Xue, Xiao Min Pan*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The generalized minimal residual (GMRES) is accelerated by the deep learning (DL) netwowrk to solve electromagnetic scattering problems. Numerical results show that the DL accelerated GMRES outperforms the traditional GMRES in terms of computational speed under the comparable accuracy.

Original languageEnglish
Title of host publication2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665452366
DOIs
Publication statusPublished - 2022
Event2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022 - Xuzhou, China
Duration: 9 Dec 202212 Dec 2022

Publication series

Name2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

Conference

Conference2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022
Country/TerritoryChina
CityXuzhou
Period9/12/2212/12/22

Fingerprint

Dive into the research topics of 'Deep Learning Accelerated GMRES Solution of Electromagnetic Scattering From Dielectric Objects'. Together they form a unique fingerprint.

Cite this