概括
基于物理学的神经网络 (PINNs) 为光学模式解决提供了一种新的方法. 第四阶衍生PINN (4DPINN) 准确地解决了全向量波导特异模式,克服了光子设备设计传统方法的局限性.
科学领域:
- 光子学和计算电磁学 电磁学
背景情况:
- 传统的光学模式解决的数值方法在几何适应性和计算效率方面面临挑战.
- 基于物理学的神经网络 (PINNs) 已经成为解决光子学前向和反向问题的强大工具.
研究的目的:
- 为准确的全向量波导特态解决方案引入和验证第四阶导数PINN (4DPINN).
- 为了证明4DPINNs在直接模式分析和光学效率计算的触角电磁场组件的解决能力.
主要方法:
- 通过整合边界条件,初始化协议和从麦克斯韦方程中获得的第四阶导数损失函数,开发了4DPINNs.
- 通过将电场分布和传播常数与分析基准进行比较,验证了4DPINNs.
- 采用自适应学习率优化,同时预测传播常数和场分布.
主要成果:
- 与用于电场分布的分析解决方案相比,4DPINNs实现了低于-12 dB的最大绝对误差和低于-50 dB的最小误差.
- 传播常数误差被限制在10-4以下,最大场分布误差低于-12dB.
- 证明了波导自身解决的高精度和广泛适用性.
结论:
- 4DPINN为全向量波导特态分析提供了一个高度准确和计算高效的方法.
- 这种方法比光子设备设计的传统数值方法具有显著的优势.
- 开发的4DPINNs对于半导体设备和光子集成电路中的应用具有相当大的价值.
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