机器学习方法用于预测连续体中受束状态诱导的法诺共振的线形
V S Gerasimov1,2, A S Kostyukov1, A E Ershov1,2
1International Research Center of Spectroscopy and Quantum Chemistry, Siberian Federal University, Krasnoyarsk, Russia, 660041.
Scientific reports
|August 25, 2025
概括
连续性中的对称保护绑定状态会在介电网格中诱导共振. 一个合模式理论公式预测了各种共振形状,机器学习准确地预测了Fano共振线形状.
科学领域:
- 物理
- 光学学
- 材料科学
背景情况:
- 连续性中的束状态 (BIC) 是局部状态,不辐射能量.
- 具有特定对称性的介电网可以容纳对称性保护的BIC.
- 光学装置中的共振对于控制光物质相互作用至关重要.
研究的目的:
- 研究介电网格中的对称性保护结合状态 (BIC) 诱导的共振.
- 开发一个理论框架来理解和控制共振线形.
- 探索机器学习用于预测共振特征.
主要方法:
- 使用合模式理论推导一种通用公式.
- 用平面内镜面对称的介电网的数值模拟.
- 应用随机森林机器学习来预测Fano共振线形.
主要成果:
- 一个两参数公式准确地描述了传导的共振形状,包括法诺,洛伦兹和反洛伦兹的形状.
- 即使没有上下对称,也始终观察到传输零值.
- 随机森林机器学习可以预测Fano共振线形状,比标准最小平方方法准确度要高得多.
结论:
- 合模式理论为分析介电网格中的BIC诱导共振提供了一个强大的框架.
- 透射率零的存在是一个一般的特征,反射率零则取决于对称性近似值.
- 机器学习提供了精确预测复杂光学共振现象的强大工具.
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