PNNL researchers are helping to better define the need for grid energy storage in future clean energy scenarios, as well as working to improve technologies for storing renewable energy so it's available when and where it's needed.
O’Neil met with members of parliament, the shadow parliament, and the UK’s national security organizations. The entire board, along with 100 invited cybersecurity professionals, attended a special event at the House of Commons.
To overcome high-performance computing bottlenecks, a research team at PNNL proposed using graph theory, a mathematical field that explores relationships and connections between a number, or cluster, of points in a space.
Three PNNL authored papers were accepted as posters to the ICLR 2023 Workshop on Physics for Machine Learning and Workshop on Mathematical and Empirical Understanding of Foundation Models.
Based on the early success of CHIRP and the urgency to build the future cybersecurity workforce, the program recently received five million dollars in funding through the FY23 Defense Appropriations Bill, via SSC.
Machine learning models help identify important environmental properties that influence how often extreme rain events occur with critical intensity and duration.