概括
我们介绍了一种使用物理信息神经网络 (PINNs) 进行光学设备反向设计的新方法. 这种方法可以有效地产生目标光学近场,而不需要预先计算的数据或相邻的梯度.
科学领域:
- 计算电磁学的计算.
- 光学设备的设计设计.
- 基于物理的机器学习.
背景情况:
- 光学设备的传统反向设计方法通常需要广泛的训练数据或计算上昂贵的辅助方法.
- 产生特定的光学近场分布对于纳米光子学和先进成像等应用至关重要.
- 现有的方法可能会在复杂的边界条件和可实现的物质约束下扎.
研究的目的:
- 为能够生成用户定义的近场图案的光学设备开发一种新的反向设计框架.
- 利用基于物理学的神经网络 (PINNs) 解决反向设计问题,以高效准确的方法.
- 为了能够发现非直观的光学结构,用于有针对性的电磁场操纵.
主要方法:
- 将有限差异赫尔姆霍尔茨方程直接嵌入PINN损失函数中,以确保波方程的合规性.
- 使用数据丢失函数来强制生成规定的近场分布.
- 整合惩罚和二元化损失,以限制设备的折射率到物理可实现的值.
主要成果:
- 基于PINN的方法准确地预测光学场,与一般化的洛伦兹-米埃理论和全波模拟进行验证.
- 该框架成功设计了用于生成目标近距离场的光学设备,包括光子纳米网和光子.
- 该方法证明了自动发现复杂,非直观的设备结构的能力.
结论:
- 基于物理学的神经网络为光学设备的反向设计提供了一种强大且数据效率高的方法.
- 拟议的方法消除了预先计算的训练数据集和特定问题附加梯度的需要.
- 该框架为设计具有任意近场特征的光学元件提供了一种多功能工具.
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