# Physics-informed neural networks for diffraction tomography

arXiv: https://arxiv.org/abs/2207.14230 · HTML: https://mlsanity.com/paper/2207.14230/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 39 references that are in the map.
- First posted on arXiv 28 Jul 2022.

## Builds on (most cited first, 9 of 9)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [DeepXDE: A deep learning library for solving differential equations](https://mlsanity.com/paper/1907.04502/) (2019)
- [Physics-informed neural networks (PINNs) for fluid mechanics: A review](https://mlsanity.com/paper/2105.09506/) (2021)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [Phase recovery and holographic image reconstruction using deep learning in neural networks](https://mlsanity.com/paper/1705.04286/) (2017)
- [Deep Learning Microscopy](https://mlsanity.com/paper/1705.04709/) (2017)
- [High-resolution 3D refractive index microscopy of multiple-scattering samples from intensity images](https://mlsanity.com/paper/1909.00023/) (2019)
- [Three-Dimensional Optical Diffraction Tomography with Lippmann-Schwinger Model](https://mlsanity.com/paper/1910.13844/) (2019)
- [Deep-learning PDEs with unlabeled data and hardwiring physics laws](https://mlsanity.com/paper/1904.06578/) (2019)

## Built on by (most cited first, 2 of 2)

- [Physics-Informed Computer Vision: A Review and Perspectives](https://mlsanity.com/paper/2305.18035/) (2023)
- [Local Conditional Neural Fields for Versatile and Generalizable Large-Scale Reconstructions in Computational Imaging](https://mlsanity.com/paper/2307.06207/) (2023)

## Abstract (arXiv)

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.