| Study on a 3D Possion's equation slover based on deep learning technique T Shan, X Dang, M Li, F Yang, S Xu, J Wu 2018 IEEE International Conference on Computational Electromagnetics (ICCEM …, 2018 | 114 | 2018 |
| Coding programmable metasurfaces based on deep learning techniques T Shan, X Pan, M Li, S Xu, F Yang IEEE journal on emerging and selected topics in circuits and systems 10 (1 …, 2020 | 99 | 2020 |
| Study on a fast solver for Poisson’s equation based on deep learning technique T Shan, W Tang, X Dang, M Li, F Yang, S Xu, J Wu IEEE transactions on antennas and propagation 68 (9), 6725-6733, 2020 | 69 | 2020 |
| Physics embedded deep neural network for solving full-wave inverse scattering problems R Guo, Z Lin, T Shan, X Song, M Li, F Yang, S Xu, A Abubakar IEEE transactions on antennas and propagation 70 (8), 6148-6159, 2021 | 48 | 2021 |
| Artificial intelligence: New frontiers in real-time inverse scattering and electromagnetic imaging M Salucci, M Arrebola, T Shan, M Li IEEE Transactions on Antennas and Propagation 70 (8), 6349-6364, 2022 | 43 | 2022 |
| Physics embedded deep neural network for solving volume integral equation: 2-D case R Guo, T Shan, X Song, M Li, F Yang, S Xu, A Abubakar IEEE Transactions on Antennas and Propagation 70 (8), 6135-6147, 2021 | 40 | 2021 |
| Solving combined field integral equation with deep neural network for 2-D conducting object R Guo, Z Lin, T Shan, M Li, F Yang, S Xu, A Abubakar IEEE Antennas and Wireless Propagation Letters 20 (4), 538-542, 2021 | 24 | 2021 |
| Phase synthesis of beam-scanning reflectarray antenna based on deep learning technique T Shan, M Li, S Xu, F Yang Progress In Electromagnetics Research 172, 41-49, 2021 | 22 | 2021 |
| Application of multitask learning for 2-D modeling of magnetotelluric surveys: TE case T Shan, R Guo, M Li, F Yang, S Xu, L Liang IEEE Transactions on Geoscience and Remote Sensing 60, 1-9, 2021 | 18 | 2021 |
| Physics-informed supervised residual learning for electromagnetic modeling T Shan, J Zeng, X Song, R Guo, M Li, F Yang, S Xu IEEE Transactions on Antennas and Propagation 71 (4), 3393-3407, 2023 | 13 | 2023 |
| Neural born iterative method for solving inverse scattering problems: 2D cases T Shan, Z Lin, X Song, M Li, F Yang, S Xu IEEE Transactions on Antennas and Propagation 71 (1), 818-829, 2022 | 12 | 2022 |
| Synthesis of refiectarray based on deep learning technique T Shan, M Li, S Xu, F Yang 2018 Cross Strait Quad-Regional Radio Science and Wireless Technology …, 2018 | 12 | 2018 |
| Physics-informed supervised residual learning for 2D electromagnetic forward modeling T Shan, X Song, R Guo, M Li, F Yang, S Xu arXiv e-prints, arXiv: 2104.13231, 2021 | 8 | 2021 |
| A new approach for solving inverse scattering problems based on physics-informed supervised residual learning T Shan, Z Lin, X Song, M Li, F Yang, S Xu 2022 16th European Conference on Antennas and Propagation (EuCAP), 1-4, 2022 | 5 | 2022 |
| Physics-Informed Supervised Residual Learning for 2-D Inverse Scattering Problems T Shan, Z Lin, X Song, M Li, F Yang, S Xu IEEE Transactions on Antennas and Propagation 71 (4), 3746 - 3751, 2023 | 2 | 2023 |
| Hardware-friendly unsupervised coding scheme for reconfigurable intelligent surface based on binary neural networks T Shan, M Li, F Yang, S Xu 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI …, 2022 | 1 | 2022 |
| Physics embedded iterative neural network for solving integral equations R Guo, Z Lin, T Shan, X Song, M Li, F Yang, S Xu, A Abubakar 2021 15th European Conference on Antennas and Propagation (EuCAP), 1-5, 2021 | 1 | 2021 |
| Solving Combined Field Integral Equations with Physics-informed Graph Residual Learning for EM Scattering of 3D PEC Targets T Shan, M Li, F Yang, S Xu IEEE Transactions on Antennas and Propagation, 2023 | | 2023 |
| Solving Combined Field Integral Equations of 3D PEC Targets Based on Physics-informed Graph Residual Learning T Shan, M Li, F Yang, S Xu 2023 XXXVth General Assembly and Scientific Symposium of the International …, 2023 | | 2023 |
| Deep learning techniques for electromagnetic forward modeling T Shan, M Li Applications of Deep Learning in Electromagnetics: Teaching Maxwell's …, 2023 | | 2023 |
Maokun LiTsinghua UniversityVerified email at tsinghua.edu.cn
