PhotonIcs and Electromagnetics Research Symposium, also known as Progress In Electromagnetics Research Symposium
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5_FocusSession.SC5: Machine Learning for Electromagnetic Inverse Problems 1

Session Organizers

Zhun Wei
Zhejiang University
China
Xudong Chen
National University of Singapore
Singapore

Session Infomation

The session welcomes all papers that are related to learning approach to electromagnetic inverse problems. Both types of inverse problems are within the scope of the session, i.e., parameter identification and inverse design. The former usually presents unique ground truth, such as in inverse scattering, localization, and CT imaging. The latter usually admits multiple solutions, such as design of photonic crystal, antenna design, and array synthesis. The session aims at presenting frontier research in the theories, computations, and applications of machine learning for solving electromagnetic inverse problems. All types of learning algorithms are welcomed: supervised and semi-supervised learning, unsupervised learning, and reinforcement learning. Uncertainty quantification (UQ) analysis is also an important topic of this session. Applications include but not limited to microwave imaging, through-wall-imaging, non-line-of-sight imaging, subsurface detection, electric impedance or capacitance tomography, biomedical imaging, near-field optical imaging, non-destructive testing/evaluation, and remote sensing.

Submitted Articles

Note: The following submitted articles are not guaranteed to be scheduled in the final program at this stage. The final presentation type and arranged session will be decided by Technical Program Committee.
Presenting Author Talk Time Paper Title | Authors | Abstract
00:00
Web
Keynote
Advances in Artificial Neural Network Techniques for Inverse Modeling of Microwave Components [Keynote]
Jing Jin (Tianjin University) *Qi-Jun Zhang (Carleton University)
00:00
Web
Invited
Learning Enabled Optical Encryption in Complex Scattering Media
Lina Zhou (The Hong Kong Polytechnic University) Yin Xiao (The Hong Kong Polytechnic University) *Wen Chen (The Hong Kong Polytechnic University)
00:00
Onsite
Focus Shaping Using Untrained Artificial Neural Network
Ze-Yang Chen (Sun Yat-sen University) Zhun Wei (Zhejiang University) *Rui Chen (Sun Yat-Sen University)
00:00
Onsite
A Tailored Semi-physics-driven Artificial Neural Network for Electromagnetic Full-wave Inversion
*Feng Han (Xiamen University) Yanjin Chen (Xiamen University) Miao Zhong (Xiamen University) Zhen Guan (Xiamen University)
00:00
Onsite
A Robust Bypass Detection Method for LED Operating States Based on PWM Interference
Xinyu Hong (Zhejiang University) Yinger Zhang (Zhejiang University) Tingjun Lai (Zhejiang University) Hengjian Ma (Zhejiang University) *Jiangtao Huangfu (Zhejiang University)
00:00
Onsite
Invited
Machine Learning-incorporated Electromagnetic Modeling and Imaging
*Kuiwen Xu (Hangzhou Dianzi University) *** ***
00:00
Web
Machine-learning-accelerated Calibration of Electromagnetic Grain Monitoring Systems
*Keeley Edwards (University of Manitoba) E. Kim (University of Manitoba) Joe LoVetri (University of Manitoba) Ian Jeffrey (University of Manitoba) Colin Gilmore (University of Manitoba)
00:00
Onsite
SAR Open Set Recognition Based on Counterfactual Framework
Xiaoyan Zhou (National University of Defense Technology) *Tao Tang (National University of Defense Technology) Yuting Cui (National University of Defense Technology) Gangyao Kuang (National University of Defense Technology)
00:00
Web
Keynote
Latest Advances in Learning-assisted Information Retrieval from Microwave Observations in Biomedical Inverse Scattering and Environmental Sensing [Keynote]
*Mahta Moghaddam (University of Southern California)
00:00
Web
Towards a Calibration-free Approach to Deep Learning based Single-incidence Inverse Scattering
*Girija Ramesan Karthik (Indian Institute of Scienc) Prasanta Kumar Ghosh (Indian Institute of Scienc)
00:00
Web
Keynote
Scalable Semiconductor Classical and Quantum Photonic Systems [Keynote]
*Jelena Vuckovic (Stanford University)

Contact us

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