2022.6.02 10:00 腾讯会议号：309-207-118

This talk applies deep learning to solve electromagnetic inverse scattering problems. Solving wave imaging problems using machine learning (ML) has attracted researchers’ interests in recent years. However, most existing works directly adopt ML as a black box. In fact, researchers have gained, over several decades, much insightful domain knowledge on wave physics and in addition some of these physical laws present well-known mathematical properties (even analytical formulas), which do not need to be learnt by training with a lot of data. This talk demonstrates that it is of paramount importance to address the problem of how profitably combining ML with the available knowledge on underlying wave physics. If time is allowed, the talk will briefly discuss the application of physics-assisted learning approaches to other inverse problems, including electric impedance tomography (EIT), radar target classification, and computational electromagnetics.