摘要
A physical-informed neural network (PINN) is employed to solve electromagnetic scattering problems which can map the incident field to scattered field directly. Numerical simulations on 2D electromagnetic scattering problems are carried out to validate the performance of PINN.
| 源语言 | 英语 |
|---|---|
| 主期刊名 | 2022 IEEE Conference on Antenna Measurements and Applications, CAMA 2022 |
| 出版商 | Institute of Electrical and Electronics Engineers |
| ISBN（电子版） | 9781665490375 |
| DOI | |
| 出版状态 | 已出版 - 2022 |
| 活动 | 2022 IEEE Conference on Antenna Measurements and Applications, CAMA 2022 - Guangzhou, 中国 期限: 14 12月 2022 → 17 12月 2022 |
出版系列
| 姓名 | IEEE Conference on Antenna Measurements and Applications, CAMA |
|---|---|
| 卷 | 2022-December |
| ISSN（印刷版） | 2474-1760 |
| ISSN（电子版） | 2643-6795 |
会议
| 会议 | 2022 IEEE Conference on Antenna Measurements and Applications, CAMA 2022 |
|---|---|
| 国家/地区 | 中国 |
| 市 | Guangzhou |
| 时期 | 14/12/22 → 17/12/22 |
指纹
探究 'Physics-informed Deep Learning to Solve Electromagnetic Scattering Problems' 的科研主题。它们共同构成独一无二的指纹。引用此
Wang, J. Y., Li, Y., Xue, B. W., & Pan, X. M. (2022). Physics-informed Deep Learning to Solve Electromagnetic Scattering Problems. 在 2022 IEEE Conference on Antenna Measurements and Applications, CAMA 2022 (IEEE Conference on Antenna Measurements and Applications, CAMA; 卷 2022-December). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CAMA56352.2022.10002575