SPIE_Advanced Photonics
Volume 4
Issue 6
Physics-informed neural networks for diffraction tomography
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.
- Amirhossein Saba
- Carlo Gigli
- Ahmed Bassam Ayoub
- Demetri Psaltis
Physics-informed neural networks for diffraction tomography (spiedigitallibrary.org)
Image created by minjeong Kim / Nanosphere
SPIE_Advanced Photonics
Volume 4
Issue 6
Physics-informed neural networks for diffraction tomography
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.
Physics-informed neural networks for diffraction tomography (spiedigitallibrary.org)
Image created by minjeong Kim / Nanosphere