Publication
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.
Mario Paolone, André Hodder, Lucien André Félicien Pierrejean, Simone Rametti
Marco Picasso, Alexandre Caboussat, Maude Girardin
Marcos Rubinstein, Farhad Rachidi-Haeri, Elias Per Joachim Le Boudec, Chaouki Kasmi, Nicolas Mora Parra, Emanuela Radici