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.
Bobbie-Jo Webb-Robertson is a leader with a PhD in decision sciences & engineering systems from Rensselaer Polytechnic Institute and experience in managing complex scientific programs and line organizations. She assumed the role 3/13/23.
Patented microchannel heat-exchange technology enables the production of hydrogen from methane, the main ingredient of natural gas, while producing 30 percent less carbon dioxide than conventional processes.
Machine learning models help identify important environmental properties that influence how often extreme rain events occur with critical intensity and duration.
PNNL is working with the Port of Seattle and Seattle City Light to assess the risks of long-term hydrogen storage that can bring clean power for decarbonization.