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.
@article{arxiv.2207.14230,
title = {Physics-informed neural networks for diffraction tomography},
author = {Amirhossein Saba and Carlo Gigli and Ahmed B. Ayoub and Demetri Psaltis},
journal= {arXiv preprint arXiv:2207.14230},
year = {2022}
}