A group of female mathematicians and computer scientists, which includes PNNL’s Emilie Purvine, has published its third paper on joint research to understand and accurately represent object relationships through metric graphs.
PNNL will lead three new grid modernization projects funded by the Department of Energy. The projects focus on scalability and usability, networked microgrids, and machine learning for a more resilient, flexible and secure power grid.
At a conference featuring the most advanced computing hardware and software, ML in its various guises was on full display and highlighted by Nathan Baker’s featured invited presentation.
Through her role in the Department of Energy’s Advanced Scientific Computing Research-supported ExaLearn project, Jenna Pope is developing deep learning approaches for finding optimal water cluster structures for a variety of applications.
Scientists at PNNL are bringing artificial intelligence into the quest to see whether computers can help humans sift through a sea of experimental data.
In today’s digital age, the rabbit hole of connected information can be not only a time sink, but downright overwhelming. Even for high-performance computers.
Twenty-four analysts from U.S. intelligence organizations met in August for a machine learning activity with PNNL researchers Nicole Nichols, Jeremiah Rounds, Lawrence Phillips, and Brian Kritzstein.
Trouble on the electric grid might start with something relatively small: a downed power line, or a lightning strike at a substation. What happens next?
Pacific Northwest National Laboratory is leading efforts to address next-generation computing’s critical role in protecting the nation from cybersecurity threats.
Network Collapse, a virtual reality science, technology, engineering, and mathematics (STEM) app developed by PNNL researchers, has won a Gold Award from the 2019 International Serious Play Award.
Researchers at PNNL are applying deep learning techniques to learn more about neutrinos, part of a worldwide network of researchers trying to understand one of the universe’s most elusive particles.
The U.S. Nuclear Regulatory Commission, U.S. Army Corps of Engineers, and PNNL partnered to complete—in record time—an environmental impact statement for the nation’s first small modular nuclear reactor, to be sited at Clinch River, Tenn.
Scientists created a fast-track tutorial that equips a neural network to tackle drug discovery and other applications where there's a shortage of precisely labeled chemical data.
Researchers used novel methods to safely create and analyze plutonium samples. The approaches could prove influential in future studies of the radioactive material, benefitting research in legacy, national security and nuclear fuels.
Scientists are exploring the use of deep neural network to interpret highly technical data related to national security, the environment and the cosmos.