Global climate change is often at the forefront of national and international discussions and controversies, yet many details of the specific contributing factors are poorly understood.
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.
PNNL helped teach the next generation of principal investigators about aerosols—tiny atmospheric particles that can affect the Earth’s climate—during the 2019 Aerosol Summer School.
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.
After 10 years, a specialized research aircraft operated by PNNL for the DOE completed is final campaign. PNNL staff are leading efforts to instrument a new plane for future research.
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.
Five years ago, in March 2014, researchers spent hours packed aboard a steamy Gulfstream-1 research aircraft as it zig-zagged between pristine air over the Amazon rainforest and polluted air nearby.
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.