[HTML][HTML] MaxwellNet: Physics-driven deep neural network training based on Maxwell's equations
Maxwell's equations govern light propagation and its interaction with matter. Therefore, the
solution of Maxwell's equations using computational electromagnetic simulations plays a …
solution of Maxwell's equations using computational electromagnetic simulations plays a …
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 …
tomographic reconstructions of biological samples. We demonstrate that by training this …
A physics-informed geometric learning model for pathological tau spread in alzheimer's disease
Tau tangles are a pathophysiological hallmark of Alzheimer's disease (AD) and exhibit a
stereotypical pattern of spatiotemporal spread which has strong links to disease progression …
stereotypical pattern of spatiotemporal spread which has strong links to disease progression …
Implementation of machine learning strategies in resonant ultrasound spectroscopy
F Giraldo Grueso - 2021 - repositorio.uniandes.edu.co
Resumen en español La reacción elástica de un material viene dictada por su tensor de
constantes elásticas. Estas constantes elásticas son una medida de las fuerzas …
constantes elásticas. Estas constantes elásticas son una medida de las fuerzas …
Optical diffraction tomography based on 3d physics-inspired neural network (PINN)
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 …
the 3D reconstruction of the refractive index (RI) for semi-transparent samples. Various …