报告题目：Machine learning for inverse scattering problems

报告人：张凯教授 

报告时间：2021年11月26日14:30-17:30

报告地点：腾讯会议960 325 713

报告摘要：In this presentation, we consider artificial neural networks for inverse scattering problems. As a working model, we consider the inverse problem of recovering a scattering object from the (possibly) limited-aperture radar cross section (RCS) data collected corresponding to a single incident field. From a geometrical and physical point of view, the low-frequency data should be able to resolve the unique identifiability issue, but meanwhile lose the resolution. On the other hand, the machine learning can be used to break through the resolution limit. By combining the two perspectives, we develop a fully connected neural network (FCNN) for the inverse problem. Extensive numerical results show that the proposed method can produce stunning reconstructions.

张凯教授2006年获吉林大学博士学位，2008年获得香港中文大学联合培养博士学位。2008-2010年，赴密歇根州立大学开展博士后工作，现为吉林大学唐敖庆特聘教授。张凯教授先后赴伊利诺伊州立大学，奥本大学，香港浸会大学，南方科技大学等开展合作研究，研究兴趣为随机偏微分方程的数值解法。主要从事随机麦克斯韦方程和随机声波方程数值方法，期权定价和套利的数值方法研究。先后主持国家自然科学基金等项目11项，发表论文50余篇。欢迎广大师生踊跃参加！