121 citations · 272 across the 8 of their papers we have counts for
4 papers · 1 filter
Programming Nonlinear Propagation for Efficient Optical Learning Machines
Ilker Oguz, Jih-Liang Hsieh, Niyazi Ulas Dinc +5
The ever-increasing demand for processing data with larger machine learning models requires more efficient hardware solutions due to limitations such as power dissipation and scala…
Predicting nonlinear optical scattering with physics-driven neural networks
Carlo Gigli, Amirhossein Saba, Ahmed Bassam Ayoub +1
Deep neural networks trained on physical losses are emerging as promising surrogates of nonlinear numerical solvers. These tools can predict solutions of Maxwell's equations and co…
Physics-informed neural networks for diffraction tomography
Amirhossein Saba, Carlo Gigli, Ahmed B. Ayoub +1
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 Helmh…
Optical Diffraction Tomography based on 3D Physics-Inspired Neural Network (PINN)
Ahmed B. Ayoub, Amirhossein Saba, Carlo Gigli +1
Optical diffraction tomography (ODT) is an emerging 3D imaging technique that is used for the 3D reconstruction of the refractive index (RI) for semi-transparent samples. Various i…