Conditional, super-resolution application is bridging the gap between coarse resolution and convective-permitting scale in earth system models using deep learning.
Argonne is employing deep neural networks to replace computationally expensive parameterizations of certain physical schemes in the Weather Research and Forecasting model.
An Argonne team has developed a machine learning approach for calibrating the center of rotation in x-ray light source tomography data that provides better accuracy than conventional imaging processing-based methods.
From studying a sea slug, researchers have demonstrated a fundamental type of learning in an inorganic system that may serve as a building block for neuromorphic computing and AI applications.
A new machine learning technique that uses data from high-energy X-ray diffraction experiments will significantly reduce model development time and human effort.