Reconfigurable intelligent surface, Metasurface, Photonics, Electromagnetic scattering, Inverse scattering, Heat transfer, Computational multi-physical fields, and Deep learning

Yinpeng Wang received the B.S. degree in Electronic and Information Engineering and the M.S. degree in Electronic Science and Technology both at Beihang University in 2020 and 2023, respectively. He is now a PhD student at National University of Singapore. From 2017 to 2018, he was a researcher at the Physical Experiment Center, Beihang University. In 2018, he worked as a research assistant at the Spintronics Interdisciplinary Center. Since 2018, he has been a member of the Institute of EMC Technology. Since 2018, Mr. Wang has published 2 academic monographs and more than 30 peer-reviewed technical papers in international journals and conferences. He serves as a reviewer for Springer, IOP, Elsevier, and IEEE journals.

2023.08-now National University of Singapore, PhD

2020.09-2023.01 Beihang University, Master of Engineering

2016.09-2020.06 Beihang University, Bachelor of Engineering

1. National Scholarship, 2021 and 2022 (Twice)

2. Top Ten Graduate Students (Highest Honor for Graduate Students in Beihang University), Jun. 2022

3. Excellent Graduates of Beijing, Jan. 2023

4. PIERS 2021 Best Student Paper Award, Nov. 2021

5. Excellent Academic Scholarship, 2017-2022 (5 times)

6. Freshman Scholarship, Sep. 2020

7. Second Prize of the National Undergraduate Mathematics Competition, Nov. 2018

8. First Prize of Beijing Physics Experiment Competition, Nov. 2018

• Books

[1] Y. Wang^#, Q. Ren, Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems, CRC Press, Boca Raton, 2023. ISBN: 9781003397830, doi: 10.1201/9781003397830. (Google Scholar citations: 11)

[2] Q. Ren^#, Y. Wang, Y. Li, S. Qi, Sophisticated Electromagnetic Forward Scattering Solver Via Deep Learning, Springer Nature, Singapore, 2022, ISBN: 9789811662607, doi: 10.1007/978-981-16-6261-4. (Google Scholar citations: 16)

• Journal Papers

[1] Y. Wang^#, Q. Dai, C. Xu, D. Li, Z. Ren, N. Liu, C. P. Ho, P. Pitchappa, and C. Lee, Machine Learning Enabled MEMS Reconfigurable Intelligent Surface for Terahertz Beam Steering and Wireless Communication, Light: Science & Applications, to be published, 2026. (SCI, JCR Q1 Top, IF=23.4)

[2] Y. Wang^#, Y. Zhan, Diffusion for Diffusion: A versatile multiphysics fields refinement framework in pollutants transportation, Water Research, vol. 289, pp. 124962, 2026. doi: 10.1016/j.watres.2025.124962 (SCI, JCR Q1 Top, IF=12.4)

[3] A. Xu^#, M. Xiao, Z. Sui, Y. Wang, D. Zheng, Y. Liu, L. Wang, H. Liu, and C. Lee, Transducers Across Scales and Frequencies: A System-Level Framework for Multiphysics Integration and Co-Design, Advanced Materials Technologies, e02093, 2026. doi: 10.1002/admt.202502093 (SCI, JCR Q1, IF=6.4)

[4] D. Li^#, W. Liu^#, Y. Wang^#, D. Zheng, C. Lee, MEMS-Enabled Reconfigurable Metasurface and Nanophotonics: Advances and Perspectives, Micro & Nano Manufacturing, vol. 2, pp. 7, 2026. doi: 10.1007/s44374-026-00015-y

[5] H. Zhou^#, D. Li, Y. Wang, and C. Lee, Plasmonic Metamaterials for Multi-Effect Enhancement: Bridging Optical, Electronic, Thermal, and Acoustic Domains. npj Metamaterials, vol. 2, pp. 14, 2026. doi: 10.1038/s44455-026-00022-z

[6] Q. Dai^#, Y. Wang, C. Xu, D. Li, P. Pitchappa, T. C. Tan, R. Singh, and C. Lee, High-Asymmetry Metasurface: A New Solution for Terahertz Resonance via Active Learning-Augmented Diffusion Model, Advanced Science, vol. 2, pp. e08610, 2026. doi: 10.1002/advs.202508610 (SCI, JCR Q1 Top, IF=14.3)

[7] Y. Wang^#, S. Zhang, Multi-receptive-field physics-informed neural network for complex electromagnetic media, Optical Materials Express, vol. 14, pp. 2740-2754,2024. doi: 10.1364/OME.533643 (SCI, JCR Q2)

[8] Y. Wang^#, Deep Multiphysics Fields Solver Established on Operator Learning Transformer and Finite Element Method, IEEE Journal on Multiscale and Multiphysics Computational Techniques, vol. 9, pp. 100-108, 2024. doi: 10.1109/JMMCT.2024.3463748 (SCI)

[9] H. Gao^#, Y. Wang^#, Q. Ren, Z. Wang, L. Deng, C. Shi and J. Li, Deep learning based time-domain inversion for high-contrast scatterers, Journal of Electromagnetic Waves and Applications, vol. 38, pp. 1844-1867, 2024. doi: 10.1080/09205071.2024.2401002 (SCI, Equally contributed)

[10] X Sun^#, B Du, Y. Wang, Q Ren, Coupled multiphysics solver for irregular regions based on graph neural network, International Journal of Thermofluids, vol. 23, pp. 100726, 2024. doi: 10.1016/j.ijft.2024.100726

[11] Y. Wang^#, H. Gao and Q. Ren, Differential Operator Approximation Based Tightly Coupled Multiphysics Solver Using Cascaded Fourier Network, Advanced Theory and Simulations, vol. 5, pp. 2200409. doi: 10.1002/adts.202200409. (SCI, JCR Q2)

[12] Y. Wang^#, N. Wang and Q, Ren, Predicting Surface Heat Flux on Complex Systems via Conv-LSTM, Case Studies in Thermal Engineering, vol. 33, pp. 101927, 2022. doi: 10.1016/j.csite.2022.101927. (SCI, JCR Q1, IF=6.4, Google Scholar citations: 23)

[13] Y. Wang^#, Q. Ren, A Versatile Inversion approach for Space/Temperature/Time-Related Thermal Conductivity via Deep Learning, International Journal of Heat and Mass Transfer, vol. 186, pp. 122444, 2022. doi: 10.1016/j.ijheatmasstransfer.2021.122444. (SCI, JCR Q1, IF=5.8, Google Scholar citations: 28)

[14] Y. Wang^#, J. Zhou, Q. Ren, Y. Li, D. Su, 3-D Steady Heat Conduction Solver via Deep Learning, IEEE Journal on Multiscale and Multiphysics Computational Techniques, vol. 6, pp. 100-108, 2021. doi: 10.1109/JMMCT.2021.3106539. (SCI, Google Scholar citations: 34)

[15] Y. Wang^#, S. Zhang, Q. Yan, F. Tang, Coupled model and flow characteristics of thermoacoustic refrigerators, Engineering Research Express, vol. 2, pp. 025016, 2020. doi:10.1088/2631-8695/ab8ba5. (SCI)

[16] S. Qi^#, Y. Wang^#, Y. Li, X. Wu, Q. Ren, Y. Ren, Two-Dimensional Electromagnetic Solver Based on Deep Learning Technique, IEEE Journal on Multiscale and Multiphysics Computational Techniques, vol. 5, pp. 83-88, 2020, doi: 10.1109/JMMCT.2020.2995811. (Equally contributed, SCI, Google Scholar citations: 127)

[17] Y. Li^#, Y. Wang^#, S. Qi, Q. Ren, L. Kang, S. D. Campbell, P. L. Werner, D. H. Werner, Predicting Scattering From Complex Nano-Structures via Deep Learning, IEEE Access, vol. 8, pp. 139983-139993, 2020, doi: 10.1109/ACCESS.2020.3012132. (Equally contributed, SCI, JCR Q2, Google Scholar citations: 82)

• Conference Papers

[1] Y. Wang^#, Q. Dai, C. Xu, D. Li, Z. Ren, L. Wang, N. Liu, C. P. Ho, P. Pitchappa, C. Lee, Machine Learning Enabled MEMS Reconfigurable Intelligent Surface for Terahertz Beam Steering and Wireless Communication, International Conference on Micro Electro Mechanical Systems (MEMS), 2026, pp. 1460-1463. doi: 10.1109/MEMS64181.2026.11419515

[2] L. Wang^#, M. Xiao, W. Liu, D. Li, Y. Wang, Y. Zhang, Y. Zhu, C. Lee, AI-Enhanced E-Nose Based on Film Bulk Acoustic Resonators for Identification of Volatile Organic Compounds, International Conference on Micro Electro Mechanical Systems (MEMS), 2026, pp. 1064-1067. doi: 10.1109/MEMS64181.2026.11419558

[3] Y. Wang^#, Y. Li, S. Qi, Q. Ren, Electromagnetic Scattering Solver for Metal Nanostructures via Deep Learning, Photonics and Electromagnetics Research Symposium (PIERS), 2021. doi: 10.1109/PIERS53385.2021.9694820. (Best Student Paper Award, EI)

[4] Y. Wang^#, N. Wang, Q. Ren, Inversion of Sophisticated Thermal Conductivity via Deep Learning, Photonics and Electromagnetics Research Symposium (PIERS), 2022. doi: 10.1109/PIERS55526.2022.9793208. (EI)

[5] Y. Wang^#, H. Gao, Q. Ren, Cascaded Network for Inversion of Electrical Conductivity in High Noise Environment, The 13th International Symposium on Antennas, Propagation and EM Theory (ISAPE), 2021. doi: 10.1109/ISAPE54070.2021.9753243. (EI)

[6] Y. Wang^#, Q. Ren, Sophisticated Electromagnetic Scattering Solver Based on Deep Learning, 2021 International Applied Computational Electromagnetics Society Symposium (ACES), 2021. doi: 10.1109/ACES53325.2021.00167. (EI)

[7] Y. Wang^#, H. Gao, Q. Ren, Electrothermal coupling solver based on cascaded Fourier network, 2022 International Applied Computational Electromagnetics Society Symposium (ACES-China), Xuzhou, China, 2022, pp. 1-2, doi: 10.1109/ACES-China56081.2022.10065334. (EI)

[8] Y. Wang^#, Q. Ren, Noise Resistant Time-domain Inversion via Cascaded Network for Human Tissues, 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI), Denver, CO, USA, 2022, pp. 1706-1707, doi: 10.1109/AP-S/USNC-URSI47032.2022.9886278. (EI)

[9] H. Gao^#, Y. Wang and Q. Ren, Time-domain Inversion Cascade Network (TICaN) for Sophisticated Scatterers, 2021 13th International Symposium on Antennas, Propagation and EM Theory (ISAPE), Zhuhai, China, 2021, pp. 1-2, doi: 10.1109/ISAPE54070.2021.9753012. (EI)

[10] H. Gao^#, Y. Wang and Q. Ren, Deep Learning Based Pixelized Forward Simulator and Inverse Designer of the Frequency Selective Surface, 2022 International Applied Computational Electromagnetics Society Symposium (ACES-China), Xuzhou, China, 2022, pp. 1-3, doi: 10.1109/ACES-China56081.2022.10064788. (EI)

[11] C. Zhang^#, Y. He, H. Gao, Y. Wang and Q. Ren, Deep Learning Enabled Inverse Design and Optimization of the Frequency Selective Surface (FSS), 2022 International Applied Computational Electromagnetics Society Symposium (ACES-China), Xuzhou, China, 2022, pp. 1-3, doi: 10.1109/ACES-China56081.2022.10064979. (EI)

[12] N. Wang^#, Y. Wang, Q. Ren, Y. Zhao and J. Jiao, Non-linear Heat Conduction Inversion Method Based on Deep Learning, 2021 International Applied Computational Electromagnetics Society (ACES-China) Symposium, Chengdu, China, 2021, pp. 1-2, doi: 10.23919/ACES-China52398.2021.9581428. (EI)

[13] Q. Ren^#, Y. Wang, J. Cao, H. Gao, Application of Deep Learning Technique in Forward and Inverse EM and Heat Conduction Problems, The 13th Asia-Pacific International Symposium on Electromagnetic Compatibility & Technical Exhibition (APEMC 2022), Beijing, China, 2022.

[14] X. Sun^#, B. Du, Y. Wang, and Q. Ren, Electromagnetic Solver for Irregular Region Based on Graph Neural Network, 2022 International Applied Computational Electromagnetics Society Symposium (ACES-China), Xuzhou, China, 2022, pp. 1-2.