基于物理信息的深度运营商网络用于模拟2D时域电磁波在各种媒体中的传播
Sooyoung Oh1, EungKyu Lee2, Sun K Hong1,3
1School of Electronic Engineering, Soongsil University, Sangdo-ro 369, Seoul 06978, South Korea.
iScience
|March 9, 2026
概括
基于物理学的深度运营者网络 (PI-DeepONet) 准确预测电磁波的传播. 与传统方法相比,这种方法为高频系统提供了更高的概括性,减少了重新培训的需要.
科学领域:
- 计算电磁学的计算.
- 应用物理学的应用物理学
- 机器学习是机器学习.
背景情况:
- 准确预测时间域电磁波 (EM) 对高频系统至关重要.
- 传统的有限差异时间域 (FDTD) 方法与大型计算领域相斗争.
- 基于物理学的神经网络 (PINNs) 提供了数据效率,但有限的概括性.
研究的目的:
- 开发一种更具通用性和高效的方法来建模二维瞬态电磁波传播.
- 研究物理知情深度操作员网络 (PI-DeepONet) 的应用,用于电磁波建模.
- 克服传统的解决方案和PINN在处理各种EM环境中的局限性.
主要方法:
- 将时间域赫尔姆霍尔茨方程纳入DeepONet架构.
- 开发了一个以物理为基础的深度运营商网络 (PI-DeepONet) 用于电磁波传播.
- 在各种激发和材料配置上训练PI-DeepONet模型.
主要成果:
- PI-DeepONet在不同来源位置和材料特性中展示了增强的概括性.
- 该模型成功地预测了自由空间和不均质介质中的波传播和散射.
- 空间时空电磁场的准确预测与FDTD模拟进行了验证.
结论:
- PI-DeepONet提供了一个强大而高效的替代模型,用于时间域EM分析.
- 该框架显示了在复杂环境中设计高频系统的巨大潜力.
- 这种方法增强了概括能力,减少了对模型再培训的需求.
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