Demetri Psaltis, Carlo Gigli, Amirhossein Saba Shirvan, Ahmed Ayoub
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 ...
SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS2022