Enhanced Detection of Nuclear Events, Thanks to Deep Learning
Scientists are exploring the use of deep neural network to interpret highly technical data related to national security, the environment and the cosmos.
PNNL Joins Science Leaders on National Stage in Seattle
PNNL researchers and professional staff led discussions ranging from biothreats and climate change to science careers at the 2020 annual meeting of the American Association for the Advancement of Science, held this year in Seattle.
PNNL Scientists Defend Against New Threats like Coronavirus
Combining its strength in biological sciences and data analytics, researchers at the Department of Energy's PNNL are working to enable a quick response to a biological incident — whether intentional, accidental or natural.
PNNL, Verizon bring 5G to National Laboratory
Verizon recently announced a partnership that will make Pacific Northwest National Laboratory the U.S. Department of Energy’s first national laboratory with Verizon 5G ultra wideband wireless technology.
New Method for Automated Control Leverages Advances in AI
Embedding the known physics of the system to be controlled in the AI model of the system is data efficient and provides safety guarantees.
Visual Sample Plan
Visual Sample Plan (VSP) is a software tool that supports the development of a defensible sampling plan based on statistical sampling theory and the statistical analysis of sample results to support confident decision making.
Visual Analytics Intern Chosen as Delegate for American Junior Academy of Sciences
Anika Halappanavar’s research into COVID-19 misinformation earned her recognition by the Washington State Academy of Sciences as one of the state’s top high school researchers.
Researchers Eye ‘Topological Signatures’ of Cyber Threats
A team of researchers at PNNL is developing a new approach to explore the higher-dimensional shape of cyber systems to identify signatures of adversarial attacks.
New AI approach bridges the “slim-data gap” that can stymie deep learning approaches
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.