A review describes the latest techniques for simulating hydropower at large scales to inform power grid planning and operations.

Hydropower generation estimated using water proxy data leads to a revision of the nation’s historical seasonal hydropower output.

Three PNNL authored papers were accepted as posters to the ICLR 2023 Workshop on Physics for Machine Learning and Workshop on Mathematical and Empirical Understanding of Foundation Models.

A new model uses machine learning to project electricity loads across the United States.

Under Secretary for Science and Innovation at the Department of Energy Geri Richmond visited PNNL.

Research.com ranked PNNL joint appointee George Karniadakis as one of the top mathematicians in the world.

Future global warming-induced changes to watershed and coastal processes can significantly affect the magnitude of fluvial-coastal compound flooding.

Advances in understanding the nature of the chemical bond are featured in this special issue of The Journal of Chemical Physics.

Vrabie co-organized the 2022 Workshop on Artificial Intelligence for Isotope R&D and Production for the Department of Energy Isotope Program.

New research finds that global warming will bring stronger and more frequent hurricanes to U.S. coasts, up by a third compared to current levels.

Mesoscale convective systems that are clustered in time and space have broader impacts on flooding than individual systems.

PNNL’s Sameera Horawalavithana joins the journals IEEE Transactions on Artificial Intelligence and Humanities and Social Sciences Communications.

Deep learning helps make predictions on the effects of the El Niño-Southern Oscillation on river flows more accurate.

Xantheas was one of four professors in 2021/2022 to be selected by the Göttingen Academy of Sciences and Humanities for this award.

More realistically representing crops and agricultural practices dramatically improves simulations of carbon and energy exchange.

Artificial intelligence may help humans and computers work together to monitor materials that could be used to make weapons.

In new work, PNNL researchers find that 10 gigatons of carbon dioxide may need to be pulled from Earth's atmosphere and oceans annually to limit global warming to 1.5 degrees. A diverse suite of carbon dioxide removal methods will be key.

Machine learning models help identify important environmental properties that influence how often extreme rain events occur with critical intensity and duration.

PNNL researchers combined deep operator networks with spectral methods to efficiently and accurately solve complex equations.

A scenario approach was used to explore the potential future role of hydropower around the globe considering the multisectoral dynamics of regional energy systems and basin-specific water resources.