摘要
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.
| 源语言 | 英语 |
|---|---|
| 主期刊名 | 2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022 |
| 出版商 | Institute of Electrical and Electronics Engineers Inc. |
| ISBN（电子版） | 9781665452366 |
| DOI | |
| 出版状态 | 已出版 - 2022 |
| 活动 | 2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022 - Xuzhou, 中国 期限: 9 12月 2022 → 12 12月 2022 |
出版系列
| 姓名 | 2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022 |
|---|
会议
| 会议 | 2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022 |
|---|---|
| 国家/地区 | 中国 |
| 市 | Xuzhou |
| 时期 | 9/12/22 → 12/12/22 |
指纹
探究 'Deep Learning Accelerated GMRES Solution of Electromagnetic Scattering From Dielectric Objects' 的科研主题。它们共同构成独一无二的指纹。引用此
Wang, J. Y., Xue, B. W., & Pan, X. M. (2022). Deep Learning Accelerated GMRES Solution of Electromagnetic Scattering From Dielectric Objects. 在 2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022 (2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACES-China56081.2022.10065250