393 citations · 1.4k across the 36 of their papers we have counts for
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Machine Learning Regularized Solution of the Lippmann-Schwinger Equation
Subeen Pang, George Barbastathis
Solution of the discretized Lippmann-Schwinger equation in the spatial frequency domain involves the inversion of a linear operator specified by the scattering potential. To regula…
A machine learning aided global diagnostic and comparative tool to assess effect of quarantine control in Covid-19 spread
Raj Dandekar, Chris Rackauckas, George Barbastathis
We have developed a globally applicable diagnostic Covid-19 model by augmenting the classical SIR epidemiological model with a neural network module. Our model does not rely upon p…
Limited-angle tomographic reconstruction of dense layered objects by dynamical machine learning
Iksung Kang, Alexandre Goy, George Barbastathis
Limited-angle tomography of strongly scattering quasi-transparent objects is a challenging, highly ill-posed problem with practical implications in medical and biological imaging,…
On the interplay between physical and content priors in deep learning for computational imaging
Mo Deng, Shuai Li, Iksung Kang +2
Deep learning (DL) has been applied extensively in many computational imaging problems, often leading to superior performance over traditional iterative approaches. However, two im…
Neural Network aided quarantine control model estimation of global Covid-19 spread
Raj Dandekar, George Barbastathis
Since the first recording of what we now call Covid-19 infection in Wuhan, Hubei province, China on Dec 31, 2019, the disease has spread worldwide and met with a wide variety of so…
Neural Network aided quarantine control model estimation of COVID spread in Wuhan, China
Raj Dandekar, George Barbastathis
In a move described as unprecedented in public health history, starting 24 January 2020, China imposed quarantine and isolation restrictions in Wuhan, a city of more than 10 millio…