Lee's paper at the August conference in Bellevue, Washington, combined weather and power outage data for three states – Texas, Michigan and Hawaii – and used a machine learning model to predict how extreme weather such as thunderstorms, floods and
The bigger the swirl, the bigger the problem — and the bigger the computing power needed to solve it.
We developed a novel uncertainty-aware framework MatPhase to predict material phases of electrodes from low contrast SEM images.
Researchers at the U.S. Department of Energy’s Oak Ridge National Laboratory are part of a multi-institutional team that will receive nearly $14 million over five years to tackle sparse computational problems in high-performance computing.
The researchers from ORNL have developed a new and faster algorithm for the graph all-pair shortest-path (APSP) problem.