• Advanced Photonics
  • Vol. 4, Issue 6, 066001 (2022)
Amirhossein Saba*, Carlo Gigli1、†, Ahmed B. Ayoub, and Demetri Psaltis
Author Affiliations
  • École Polytechnique Fédérale de Lausanne, Optics Laboratory, Lausanne, Switzerland
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    DOI: 10.1117/1.AP.4.6.066001 Cite this Article Set citation alerts
    Amirhossein Saba, Carlo Gigli, Ahmed B. Ayoub, Demetri Psaltis. Physics-informed neural networks for diffraction tomography[J]. Advanced Photonics, 2022, 4(6): 066001 Copy Citation Text show less

    Abstract

    We propose a physics-informed neural network (PINN) 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 PINNs can be generalized for any forward and inverse scattering problem.
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    Amirhossein Saba, Carlo Gigli, Ahmed B. Ayoub, Demetri Psaltis. Physics-informed neural networks for diffraction tomography[J]. Advanced Photonics, 2022, 4(6): 066001
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