Physics-Inspired Convolutional Neural Network for Solving Inverse Scattering Problems

讲座名称：Physics-Inspired Convolutional Neural Network for Solving Inverse Scattering Problems

讲座时间：2019-09-24 9:00:00

讲座地点：北校区西大楼III-412

讲座人：陈旭东

讲座人介绍：

陈旭东，IEEE电磁学会会士，分别于1999年和2001年获得中国浙江大学电气工程学士和硕士学位，2005年获得美国麻省理工大学剑桥分校博士学位。自2005年起，他一直在新加坡国立大学工作。他发表了150篇期刊论文，根据ISI科学网（SCI）其总引文4900+。他撰写了《电磁逆散射的计算方法》（Wiley IEEE，2018）一书。他的研究兴趣主要包括电磁逆散射、传感和数据融合、光学/红外/微波扫描显微镜、光学加密和超材料。他在各种会议上组织了20多次关于逆散射和成像的会议。他是10余次会议组织委员会的成员，担任总主席、TPC主席、奖励委员会主席等。自2015年以来，他一直是《IEEE Transactions on Microwave Theory and Techniques》的副主编。陈博士是2010年国际科学联合会青年科学家奖和IEEE ICCEM会议最佳论文奖的获得者。

讲座内容：

The talk aims to solve a full-wave inverse scattering problem (ISP), which is a quantitative imaging problem, i.e., to reconstruct the permittivities of dielectric scatterers from the knowledge of measured scattering data. This talk proposes the convolution neural network (CNN) technique to solve full-wave ISPs. In order to make machine learning more powerful, a deep understanding of the corresponding forward problem is important. In solving ISP, the concept of induced current plays an essential role in the proposed CNN technique, which enables us to design the architecture of learning machine such that unnecessary computational effort spent in learning wave physics is minimized or avoided. Numerical simulations demonstrated that the proposed CNN scheme outperforms a brute-force application of CNN. The proposed deep learning inversion scheme is promising in providing quantitative images in real time.

主办单位：发展规划部