PISC-Net:一个全面的神经网络框架,用于预测Metasurface红外辐射光谱
Changsheng Li1, Jincheng Chen1, Qunqing Lin1,2
1School of Energy and Power Engineering, Nanjing University of Science and Technology, Nanjing 210094, PR China.
ACS applied materials & interfaces
|July 31, 2024
概括
这项研究引入了一种新的AI网络,用于快速的红外超表面频谱预测,大大降低了计算成本. PISC-Net准确地预测了实验结果,证明了其在超表面设计中的实际应用.
科学领域:
- 电磁波控制器的电磁波控制器
- 超材料和纳米光子学
- 物理学中的人工智能
背景情况:
- 多功能超表面提供了对电磁波的先进控制.
- 传统的超表面设计依赖于计算上昂贵的数值模拟.
- 准确预测红外辐射光谱对于地表应用至关重要.
研究的目的:
- 开发一个快速和准确的预测方法,用于超表面的红外辐射光谱.
- 为了降低与传统的超表面设计相关的计算成本.
- 提高预测网络对地表特征的概括能力.
主要方法:
- 介绍物理洞察力自我纠正卷积网络 (PISC-Net).
- 参数通信模块的集成和物理机制识别的先验知识.
- 开发一个数据集构建策略,用于精确的吸收频段调整 (3-14μm) 和使用转移学习用于大周期元表面.
主要成果:
- PISC-Net 实现了红外辐射频谱的快速预测,具有很高的概括能力.
- 该网络只使用模拟数据准确预测实验结果,并通过比较分析验证.
- 预测的平均平均平方误差小于4%.
结论:
- PISC-Net为超表面设计提供了传统模拟方法的计算效率高的替代方案.
- 拟议的方法证明了人工智能的潜力,可以准确地预测实验性的超表面行为.
- 这项工作促进了具有成本效益的开发和精确调整跨广泛的光谱范围的 metasurfaces.
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