Publications

      Journal Papers

1. Shan T, Zen J, Song X, et al. "Physics-informed Supervised Residual Learning for Electromagnetic Modeling," in IEEE Transactions on Antennas and Propagation, doi: 10.1109/TAP.2023.3245281.

2.Shan T, Lin Z, Song X, et al. "Physics-informed Supervised Residual Learning for 2D Inverse Scattering Problem," in IEEE Transactions on Antennas and Propagation, doi: 10.1109/TAP.2023.3242372.

3.Shan T, Lin Z, Song X, et al. Neural Born Iterative Method For Solving Inverse Scattering Problems: 2D Cases, in IEEE Transactions on Antennas and Propagation, 2022, doi: 10.1109/TAP.2022.3217333.

4.Shan T, Guo R, Li M, et al. Application of Multitask Learning for 2-D Modeling of Magnetotelluric Surveys: TE Case[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 1-9.

5.  Shan T, Pan X, Li M, et al. Coding programmable metasurfaces based on deep learning techniques[J]. IEEE journal on emerging and selected topics in circuits and systems, 2020, 10(1): 114-125.

6. Shan T, Tang W, Dang X, et al. Study on a fast solver for Poisson’s equation based on deep learning technique[J]. IEEE Transactions on Antennas and Propagation, 2020, 68(9): 6725-6733.

7. Shan T, Li M, Xu S, et al. Phase Synthesis of Beam-Scanning Reflectarray Antenna Based on Deep Learning Technique[J]. Progress In Electromagnetics Research, 2021, 172: 41-49.

8. Salucci M, Arrebola M, Shan T and Li M,” Artificial Intelligence: New Frontiers in Real-Time Inverse Scattering and Electromagnetic Imaging,” in IEEE Transactions on Antennas and Propagation, vol. 70, no. 8, pp. 6349-6364, Aug. 2022, doi: 10.1109/TAP.2022.3177556.

9.  Guo R, Shan T et al., ”Physics Embedded Deep Neural Network for Solving Volume Integral Equation: 2-D Case,” in IEEE Transactions on Antennas and Propagation, vol. 70, no. 8, pp. 6135-6147, Aug. 2022, doi: 10.1109/TAP.2021.3070152.

10. Guo R, Lin Z, Shan T et al., ”Physics Embedded Deep Neural Network for Solving Full-Wave Inverse Scattering Problems,” in IEEE Transactions on Antennas and Propagation, vol. 70, no. 8, pp. 6148-6159, Aug. 2022, doi: 10.1109/TAP.2021.3102135.

11. Guo R, Lin Z, Shan T et al., ”Solving Combined Field Integral Equation With Deep Neural Network for 2-D Conducting Object,” in IEEE Antennas and Wireless Propagation Letters, vol. 20, no. 4, pp. 538-542, April 2021, doi: 10.1109/LAWP.2021.3056460.

      Conference Proceedings and Abstracts

1. Shan T, Li M, Yang F and Xu S, ”Hardware-friendly Unsupervised Coding Scheme for Reconfigurable Intelligent Surface Based on Binary Neural Networks,” 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI), 2022, pp. 449-450, doi: 10.1109/APS/USNC-URSI47032.2022.9887254.

2. Shan T, Lin Z, Song X, et al. A New Approach for Solving Inverse Scattering Problems Based on Physics informed Supervised Residual Learning[C]//2022 16th European Conference on Antennas and Propagation (EuCAP). IEEE, 2022: 1-4.

3. Shan T, Song X, Guo R, et al. Physics-informed Supervised Residual Learning for Electromagnetic Modeling[C]//2021 International Applied Computational Electromagnetics Society Symposium (ACES). IEEE, 2021: 1-4.

4. Shan T, Li M, Xu S, et al. Synthesis of reflectarray based on deep learning technique[C]//2018 Cross Strait Quad-Regional Radio Science and Wireless Technology Conference (CSQRWC). IEEE, 2018: 1-2.

5. Shan T, Dang X, Li M, et al. Study on a 3D Possion’s equation slover based on deep learning technique[C]//2018 IEEE International Conference on Computational Electromagnetics (ICCEM). IEEE, 2018: 1-3.

6. Zeng J, Shan T, Li M, et al. Neural Contrast Source Inversion Method Based on Single-frequency Data, 2022 International Applied Computational Electromagnetics Society (ACES-China) Symposium (accepted)

7. Shao T, Shan T, Li M, et al. A Poisson’s Equation Solver Based on Neural Network Precondtioned CG Method, 2022 International Applied Computational Electromagnetics Society (ACES-China) Symposium (accepted)

8. Tang W, Shan T, Dang X, et al. Study on a Poisson’s equation solver based on deep learning technique[C]//2017 IEEE Electrical Design of Advanced Packaging and Systems Symposium (EDAPS). IEEE, 2017: 1-3.