[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.
[2] Q. Ren#, Y. Wang, Y. Li, S. Qi, Sophisticated Electromagnetic Forward Scattering Solver Via Deep Learning, Springer, Singapore, 2022, ISBN: 9789811662607, doi: 10.1007/978-981-16-6261-4.
[1] 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. (SCIE, JCR Q2, IF=4.105)
[2] 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. (SCIE, JCR Q1, IF=6.268)
[3] 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. (SCIE, JCR Q1, IF=5.431)
[4] 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. (ESCI, Google Scholar citations: 13)
[5] 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. (EI)
[6] 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, ESCI, Google Scholar citations: 65)
[7] 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, SCIE, JCR Q2, IF=3.476, Google Scholar citations: 41)
[1] 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)
[2] 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)
[3] 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)
[4] 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)
[5] 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)
[6] 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)
[7] 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)
[8] 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)
[9] 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)
[10] 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)
[11] 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.
[12] 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.