ICCT 2022 Invited Speaker

Shaolin Liao, Sun Yat-sen University, China
廖少林, 中山大学

Shaolin Liao (Senior Member, IEEE) received his BS degree in materials science and engineering from Tsinghua University, Beijing, China, in 2000, and the PhD degree in electrical engineering from the University of Wisconsin-Madison, Madison, Wisconsin, in 2008. He is currently a Professor of School of Electronics and Information Technology (School of Microelectronics), Sun Yat-Sen University (SYSU), Guangzhou, Guangdong China, as well as an Adjunct Faculty of Department of Electrical and Computer Engineering, Illinois Institute of Technology (IIT), Chicago, IL USA. Before joining SYSU, he worked as a R&D staff with Argonne National Laboratory (2010-2019) and a Professor of Research and Adjunct Faculty of IIT (2019-2021).

报告题目：智算电磁学（AI-CEM）
智算电磁学（AI-CEM）运用人工智能（AI）强大的优化能力来提升计算电磁学(CEM: Computational Electromagnetics)的模拟仿真效果和优化设计潜能，能在新一代5G+/6G无线通信中发挥重要作用，包括但不限于超大规模天线阵列（massive MIMO），智能超表面（RIS: Reconfigurable Intelligent Surfaces），MIMO信道模拟仿真和先进测量技术，以及雷达感知与成像等。廖少林教授为2021年中山大学海外引进“百人计划”领军人才，同时兼任美国伊利诺伊理工大学讲席教授，长期致力于先进的CEM快速算法研究，先后研发出多款10-100倍速CEM快速算法，包括比传统矩量法（MOM: Method of Moments）快10倍速的乒乓迭代法，把传统物理光学（PO: Physics Optics）准确率提升100倍的物理光学循环迭代法（IPO: Iterative Physics Optics），比逐点积分法提升100倍速的泰勒插值快速傅里叶变换法（Taylor-FFT）等。近年来，廖少林教授的团队开始探索基于严格全波麦克斯韦方程（Maxwell’s Equations）的通用AI-CEM算法，以解决任意规模、任意复杂度的电磁结构和应用模拟仿真难题。