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.
PNNL’s autonomous fish body double, Sensor Fish, and the miniature version, Sensor Fish Mini, were used to evaluate a special screen. Researchers found the screen provides safe downstream passage for fish at irrigation structures.
A PNNL study that evaluated the use of friction stir technology on stainless steel has shown that the steel resists erosion more than three times that of its unprocessed counterpart.
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.
Three PNNL fish researchers recently published a video journal article on how to properly implant miniature acoustic tags in juvenile Pacific lamprey and American eel and how the tags could benefit migration.
A new paper found that hydropower turbines with composite blades generate about 20 percent more power than turbines with traditional stainless steel blades at the same flow rate.
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 at PNNL used key metrics to develop visualizations that show how the combined effects of climate change on hydropower and load influence the frequency, duration, and severity of power shortfalls.
Scientists are exploring the use of deep neural network to interpret highly technical data related to national security, the environment and the cosmos.
