As COVID-19 was limiting in-person contact, halting travel, and creating additional barriers, researchers at PNNL were working to find solutions on how they could still get work done while establishing new safety protocols.
PNNL researchers have shown an improved binarized neural network can deliver a low-cost and low-energy computation to help the performance of smart devices and the power grid.
The project received an Innovative and Novel Computational Impact on Theory and Experiment (INCITE) award, a highly competitive U.S. Department of Energy Office of Science program.
Researchers introduced a simulated carbon cycle to the Energy Exascale Earth System Model, broadening its utility and enabling new research directions.
Differences in the rainfall intensity of mesoscale convective systems and other types of warm—season rainfall in the central United States lead to differences in their impacts over land.