peiqin liu

Prior Knowledge-Guided Deep Learning-Enabled Synthesis Method for Metantennas

As the rapid development of Artificial Intelligence (AI) technologies, antenna engineering is undergoing a tremendous revolution. Deep learning (DL) algorithms have been employed to greatly increase the degrees of freedom (DoF) in metantenna design and provide a brand-new opportunity for expanding the boundary of antenna engineering. This talk will highlight the recent advances in DL-enabled synthesis method for metantennas. Prior knowledge (PK), including well-known fundamental electromagnetic theorems and experience in antenna engineering, are purposely integrated into DL algorithms to guide and speed up the metantenna design. The PK-DL synthesis method surpasses the limitation of traditional design techniques and breaks the performance ceiling of conventional metalens antennas. Two metalens antennas will be demonstrated to validate the PK-DL synthesis method. The fusion of PK-DL synthesis method and metantennas not only represents a significant leap forward but also heralds an exciting future for antenna engineering.

Peiqin LIU received the B.E. degrees from the University of Electronic Science and Technology of China, in 2014, and the Ph.D. degree in Electronic Engineering from Tsinghua University, Beijing, China, in 2019. Since September 2019, he has been with National University of Singapore, where he is a Research Fellow. His current research interests include metamaterial and antenna theory, particularly in novel metasurface with machine-learning methods. Dr. Liu has authored and co-authored more than 30 technical papers published in international journals and conferences and holds 5 granted Chinese patents. He is the recipient of Young Antenna Scientist Award of 2023 Singapore Workshop on Antennas (SWA) the Outstanding Student Paper Award of 2018 Cross Strait Quad-Regional Radio Science and Wireless Technology Conference (CSQRWC) the Best Student Paper Award of 2017 Asia-Pacific Conference on Antennas and Propagation (APCAP) the Best Student Paper Award of 2017 National Conference on Antennas (NCANT).