论文标题
一种物理信息的深度学习方法，用于在波导中定位源
A physically-informed Deep-Learning approach for locating sources in a waveguide
论文作者
论文摘要
逆源问题对于声学，地球物理学，非破坏性测试等的许多应用都是至关重要的。传统成像方法的分辨率限制遭受了分辨率的限制，从而阻止了与发射波长相比的区别。在这项工作中，我们提出了一种基于物理知识的神经网络来解决源重新关注问题的方法，构建了一种新颖的损失术语，该损失术语促进了网络的超分辨能力，并基于波浪传播的物理学。我们在二维矩形波导中通过沿垂直横截面的波场记录的测量值进行成像的设置进行了成像的设置。结果表明，即使将彼此靠近时，该方法的能力也可以高精度近似于源的位置。
Inverse source problems are central to many applications in acoustics, geophysics, non-destructive testing, and more. Traditional imaging methods suffer from the resolution limit, preventing distinction of sources separated by less than the emitted wavelength. In this work we propose a method based on physically-informed neural-networks for solving the source refocusing problem, constructing a novel loss term which promotes super-resolving capabilities of the network and is based on the physics of wave propagation. We demonstrate the approach in the setup of imaging an a-priori unknown number of point sources in a two-dimensional rectangular waveguide from measurements of wavefield recordings along a vertical cross-section. The results show the ability of the method to approximate the locations of sources with high accuracy, even when placed close to each other.
