Physics-informed neural networks for diffraction tomography

2022/07/28 by Amirhossein Saba, Carlo Gigli, Saba, Amirhossein +5 · 1 citation
Earth and Planetary Sciences · Engineering · #Artificial Intelligence (cs.AI) #Computational Engineering #FOS: Computer and information sciences #FOS: Physical sciences #Finance #Geophysical Methods and Applications #Optics (physics.optics) #Seismic Imaging and Inversion Techniques #Seismic Waves and Analysis #and Science (cs.CE)

paper · pdf · doi:10.48550/arxiv.2207.14230

openalex publication_date 2022/07/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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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