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arXiv · 2207.14230

Physics-informed neural networks for diffraction tomography

Amirhossein Saba·Carlo Gigli·Ahmed B. Ayoub·Demetri Psaltis
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Abstract

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.

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BibTeXRIS

Amirhossein Saba, Carlo Gigli, Ahmed B. Ayoub, Demetri Psaltis. 2022-07-28. Physics-informed neural networks for diffraction tomography. https://arxiv.org/abs/2207.14230

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