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.
Ann Lesperance has been invited to continue her role on the Domestic Preparedness advisory board, which convenes multidisciplinary subject matter experts to support the Domestic Preparedness Journal's editorial plan.
Physicist Emily Mace will share her science journey and an interactive presentation about her current research with middle school and high school students from across the country at the National Science Bowl.
Machine learning models help identify important environmental properties that influence how often extreme rain events occur with critical intensity and duration.