基于数据增强的代的少量样本学习算法,对2D可编程合元材料进行反向设计
Zeyu Zhao1, Jie You2, Jun Zhang1
1State Key Laboratory of High Performance Computing, College of Computer, National University of Defense Technology, 410073, Changsha, China.
Nanophotonics (Berlin, Germany)
|December 5, 2024
概括
一个新的算法,数据增强的代少数样本 (DEIFS),可以快速反向设计2D合元材料. 这种方法加速了复杂结构的创建,用于先进的光学应用.
科学领域:
- 超材料科学科学 超材料科学
- 计算光子学 计算光子学
- 纳米光子学 纳米光子学
背景情况:
- 超材料通过工程结构提供独特的光学特性.
- 对于先进的光学设备而言,奇拉元材料的反向设计至关重要,但计算密集.
- 现有的方法通常需要大量的数据集和大量的计算资源.
研究的目的:
- 开发一个准确和高效的算法,用于反向设计多形状的2D性超材料.
- 为了降低元材料逆向设计中的计算成本和数据依赖性.
- 为了提高反向设计算法的解释性和概括能力.
主要方法:
- 一个数据增强的代少数样本 (DEIFS) 算法,结合了数据增强和代预测.
- 使用严格的合波分析 (RCWA) 来生成训练数据.
- 使用前预测网络来生成伪数据并加强数据集.
- 基于循环二元化 (CD) 谱的几何参数的代精细化.
主要成果:
- DEIFS算法实现了精确的反向设计,数据集大小显著减少 (比以前的深度学习方法少1~2个数量级).
- 证明了多个2D合元材料形状的快速反向设计,使各种光线操纵成为可能.
- 通过分析几何参数对手术反应的影响,提高了逆向设计过程的可解释性.
结论:
- DEIFS提供了一个更快,更简单,更有效的数据替代传统代优化对元材料逆向设计.
- 该算法显示出在光学编码和信息处理方面的应用潜力很大.
- 这项研究彻底描述了DEIFS在超材料中的手术效应的灵活性,可解释性和概括性.
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