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.
Two forms of magnesium material were processed into tubing using PNNL’s Shear Assisted Processing and Extrusion™ technology. Both materials were found to have quite similar and improved properties—even though they began vastly different.
Scientists at PNNL are bringing artificial intelligence into the quest to see whether computers can help humans sift through a sea of experimental data.
B3? E4? Remember the board game Battleship? One player suggests a set of coordinates to another, hoping to find the elusive location of an unseen vessel.That is a good place to start in assessing the search for dark matter.
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.
A PNNL technology enables automated Economic Dispatch, which coordinates the use of energy in a manner that enhances distributed generation, efficiency, renewables, and grid reliability.
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 Laboratory Director Steve Ashby attended an event marking the 20th anniversary of the Department of Energy’s National Nuclear Security Administration Nuclear Smuggling Detection and Deterrence program.
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.