As the mission space of NA-213 Office of Nuclear Detection and Deterrence continues to evolve away from traditional stationary monitoring at borders and ports, the need for a solution to maintain situational awareness is critical.
Researchers developed an automated scanning probe microscopy (SPM) platform to rapidly find regions of interest.
As the growth of data sizes continues to outpace computational resources, there is a pressing need for data reduction techniques that can significantly reduce the amount of data and quantify the error incurred in compression.
A research team from ORNL and Pacific Northwest National Laboratory has developed a deep variational framework to learn an approximate posterior for uncertainty quantification.
