Optica Open
arXiv.svg (5.58 kB)

Physics-informed neural networks for diffraction tomography

Download (5.58 kB)
posted on 2023-01-12, 15:57 authored by Amirhossein Saba, Carlo Gigli, Ahmed B. Ayoub, Demetri Psaltis
We propose a physics-informed neural network as the forward model for tomographic reconstructions of biological samples. We demonstrate that by training this network with the Helmholtz equation as a physical loss, we can predict the scattered field accurately. It will be shown that a pretrained network can be fine-tuned for different samples and used for solving the scattering problem much faster than other numerical solutions. We evaluate our methodology with numerical and experimental results. Our physics-informed neural networks can be generalized for any forward and inverse scattering problem.



This arXiv metadata record was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv.

Usage metrics