报告人：张凯（吉林大学）

邀请人：李东方

报告时间：2021年12月13日（星期一）14:00-16:00

报告地点：Zoom ID: 976 8652 7522  Passcode: 422458

报告题目：Machine learning for inverse scattering problems

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.

报告人简介：张凯，吉林大学数学学院教授、博士生导师。主要研究兴趣为随机偏微分方程的数值解法。主要从事SPDE控制优化问题的数值方法、期权定价、随机麦克斯韦方程和随机声波方程的研究。