Filtered by Chemical Physics, Data Analytics & Machine Learning, Geothermal Energy, Hydrogen & Fuel Cells, Radiological & Nuclear Detection, and Renewable Energy
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.
Pumped-storage hydropower offers the most cost-effective storage option for shifting large volumes of energy. A PNNL-led team wrote a report comparing cost and performance factors for 10 storage technologies.
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.
Researchers at the Department of Energy’s Pacific Northwest National Laboratory and Sandia National Laboratories have joined forces to reduce costs and improve the reliability of hydrogen fueling stations.
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.
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.