一种基于物理的深度学习方法,用于定位波导中的源
Adar Kahana1, Symeon Papadimitropoulos1, Eli Turkel1
1Department of Applied Mathematics, Tel Aviv University, Tel Aviv 69978, Israel.
The Journal of the Acoustical Society of America
|October 24, 2023
概括
这项研究引入了一种新的物理信息神经网络方法,以克服逆源问题的分辨率限制. 该技术准确地定位了距离很近的源头,改善了声学和地球物理学等领域的成像.
科学领域:
- 波浪物理学的波浪物理.
- 计算电磁学的计算.
- 应用数学 应用数学 应用数学
背景情况:
- 反向源问题在声学,地球物理学和非破坏性测试中至关重要.
- 传统的成像技术面临着分辨率限制,阻碍了区分离波长更近的源的能力.
研究的目的:
- 为逆源问题开发超高分辨率成像方法.
- 用波传播物理学来解决区分距离近的源头的挑战.
主要方法:
- 实现物理信息的神经网络 (PINNs).
- 开发一种新的损失术语,将波传播物理纳入超分辨率.
- 在2D矩形波导中对未知数量的点源进行成像的应用.
主要成果:
- 通过PINN方法,成功地以高准确度接近源位置.
- 该方法展示了超高分辨率的能力,可以区分比波长更近的源.
- 通过沿着垂直横截面的波场记录实现了有效的成像.
结论:
- 物理信息的神经网络为克服逆源问题的分辨率限制提供了强大的解决方案.
- 新的损失术语增强了网络实现超分辨率的能力.
- 这种方法显示了高级成像应用的巨大潜力.
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