Lun
02
Feb
Seminari e Convegni
Recent progress in Al enhanced electromagnetic inverse scattering problems
On 2 February 2026 at 2:30 p.m., will take place a seminar entitled Recent progress in AI-enhanced electromagnetic inverse scattering problems, held by professor Yunyun Hu.
Abstract
Electromagnetic inverse scattering problems (ISPs) aims to determine the spatial position and physical information of scatterers from measured electromagnetic fields data. It has a wide range of applications in many fields such as medical imaging, geophysical exploration, non-destructive imaging and etc. However, due to the ill-posedness and nonlinearity of the electromagnetic inversion problem, accurate and efficient inversion are highly challenging. Many conventional inverse methods have been developed to solve the ISPs, but they generally suffer from high computational cost and slow convergence, hindering its applications. With the rapid development of deep learning methods, deep neural networks (DNNs) have been increasingly explored for ISPs and demonstrate promising performance. The seminar will review recent progress in AI-enhanced electromagnetic ISPs and present the ongoing work on improving both imaging efficiency and accuracy through physics-driven, unsupervised learning approaches.
Speaker: Professor Yunyun Hu, College of Electronic and Information Engineering, Tongji University (Shanghai)
The seminar is open to everyone and will take place in person.
Abstract
Electromagnetic inverse scattering problems (ISPs) aims to determine the spatial position and physical information of scatterers from measured electromagnetic fields data. It has a wide range of applications in many fields such as medical imaging, geophysical exploration, non-destructive imaging and etc. However, due to the ill-posedness and nonlinearity of the electromagnetic inversion problem, accurate and efficient inversion are highly challenging. Many conventional inverse methods have been developed to solve the ISPs, but they generally suffer from high computational cost and slow convergence, hindering its applications. With the rapid development of deep learning methods, deep neural networks (DNNs) have been increasingly explored for ISPs and demonstrate promising performance. The seminar will review recent progress in AI-enhanced electromagnetic ISPs and present the ongoing work on improving both imaging efficiency and accuracy through physics-driven, unsupervised learning approaches.
Speaker: Professor Yunyun Hu, College of Electronic and Information Engineering, Tongji University (Shanghai)
The seminar is open to everyone and will take place in person.