Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | 2022 IEEE Conference on Antenna Measurements and Applications, CAMA 2022 |
| Publisher | Institute of Electrical and Electronics Engineers |
| ISBN (Electronic) | 9781665490375 |
| DOIs | |
| Publication status | Published - 2022 |
| Event | 2022 IEEE Conference on Antenna Measurements and Applications, CAMA 2022 - Guangzhou, China Duration: 14 Dec 2022 → 17 Dec 2022 |
Publication series
| Name | IEEE Conference on Antenna Measurements and Applications, CAMA |
|---|---|
| Volume | 2022-December |
| ISSN (Print) | 2474-1760 |
| ISSN (Electronic) | 2643-6795 |
Conference
| Conference | 2022 IEEE Conference on Antenna Measurements and Applications, CAMA 2022 |
|---|---|
| Country/Territory | China |
| City | Guangzhou |
| Period | 14/12/22 → 17/12/22 |
Keywords
- Electromagnetic Scattering
- Machine Learning
- Physical-informed Neural Network
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Wang, J. Y., Li, Y., Xue, B. W., & Pan, X. M. (2022). Physics-informed Deep Learning to Solve Electromagnetic Scattering Problems. In 2022 IEEE Conference on Antenna Measurements and Applications, CAMA 2022 (IEEE Conference on Antenna Measurements and Applications, CAMA; Vol. 2022-December). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CAMA56352.2022.10002575