报告简介  Abstract

Composed of a large number of sub-wavelength unit cells with designable geometries, metamaterials have been widely studied to achieve extraordinary advantageous and unusual optical properties. However, ordinary computer simulator requires a time-consuming fine-tuning to find a proper design of metamaterial for a specific optical property, making the design stage a critical bottleneck in large scale applications of metamaterials. This study investigates the metamaterial design under the framework of computer experiments, with emphasis on dealing with the challenge of designing numerous unit cells with functional responses simultaneously, which is not common in traditional computer experiments. We formulate the multiple related design targets as a multi-target design problem. Leveraging on the similarity between different designs, we propose an efficient Bayesian optimization strategy with a parsimonious surrogate model and an integrated acquisition function to design multiple unit cells with very few function evaluations. A wide range of simulations confirm the effectiveness and superiority of the proposed approach compared to the naive strategies where the multiple unit cells are dealt with separately or sequentially. Such a rapid design strategy has the potential to greatly promote large scale applications of metamaterials in practice. The developed method also has the potential to be applied to other problems with similar structure.

嘉宾简介  About the Speaker 

2008年博士毕业于北京大学，同年进入哈佛大学统计系从事研究工作，历任博士后、副研究员。2013年9月加入清华大学任教至今，现为清华大学统计学研究中心长聘副教授、执行主任。主要从事统计学理论和方法的研究，并致力于推动相关研究工作在生物医学、人工智能、人文及工程等领域的应用。2014年入选国家高层次人才计划，2016年获“科学中国人年度人物”荣誉称号，2018年受聘“北京智源人工智能研究院”担任人工智能数理基础方向的“智源研究员”。他是国际计算统计学会亚太地区分会理事、中国现场统计研究会计算统计分会理事长、中国青年统计学家协会副会长、中国人工智能学会智慧医疗专业委员会副主任委员，还担任国际统计学杂志 Statisca Sinica 副主编，以及《数字人文》、《应用概率统计》、《应用数学与力学》等期刊的编委。

讲座海报 Poster