Inverse design of metasurfaces using deep learning neural networks

使用深度学习神经网络进行超表面逆向设计

• 批准号: 2888733
• 金额: --
• 依托单位: University of Southampton
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2888733
• 关键词: Inverse; design; metasurfaces; using; deep

Deep learning artificial neural networks have seen a remarkable development in recent years and hold promise for a variety of applications in the field of nanophotonics. In this project the student will explore the use of deep learning techniques in nanophotonic systems at the frontiers of the field. Techniques of interest include the use of Physics informed networks to improve the quality of solutions, as well as recently proposed neural-adjoint optimization schemes. The student will explore how these techniques can be applied to current topics in optical metasurfaces and the study of light-matter coupling in hybrid quantum-electromagnetic systems. Theoretical and computational work will be complemented with proof of principle experiments involving the design of nanosystems that can be fabricated in our cleanroom facilities and their subsequent optical studies.

深度学习人工神经网络近年来取得了显着的发展，并有望在纳米光子学领域的各种应用。在这个项目中，学生将探索深度学习技术在该领域前沿的纳米光子系统中的应用。感兴趣的技术包括使用物理信息网络来提高解决方案的质量，以及最近提出的神经伴随优化方案。学生将探索这些技术如何应用于光学超颖表面的当前主题以及混合量子电磁系统中光物质耦合的研究。理论和计算工作将与涉及纳米系统设计的原理实验证明相补充，这些纳米系统可以在我们的洁净室设施及其随后的光学研究中制造。