Rapid Design of Metamaterials via Statistical Learning and Deep Learning

      报告专家：杨洋（南开大学）

      报告时间：2023年10月31日上午10：00-11：00

      报告地点：腾讯会议：460-613-636

     报告摘要：Composed of a large number of subwavelength 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-consumingfine-tuning tofind a proper design of metamaterial for a specific optical property, making the design stage a critical bottleneck in large scale applications of metamaterials. In this talk, we investigate the metamaterial design from two different perspectives, which are the framework of computer experiments and deep neural networks, respectively. On one hand, we formulate the multiple related design targets as a multitask 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. On the other hand, we propose a Functional Response Conditional Variational Auto-Encoder (FR-CVAE), which takes complex functional response curves as inputs and generates high-quality microstructures whose physical responses match well with the given inputs, to achieve the inverse design of EM metamaterials. Thorough experimental studies demonstrate that the proposed methods are effective and practical for the rapid design of metamaterials.

      专家简介：杨洋，博士，南开大学特聘副研究员。2011-2015年本科就读于四川大学数学学院。2015-2020年博士就读于清华大学数学科学系，师从刘军教授和邓柯副教授。2020-2022年在腾讯微信担任算法研究员，研究自然语言处理领域的深度学习算法。2022年至今任职于南开大学统计与数据科学学院，研究方向为贝叶斯统计、计算机实验设计和深度学习。

      邀请人：宋恩彬

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