Learning climate
A Colorado State fellow employs machine learning for climate modeling, putting provenance behind predictions.
A Colorado State fellow employs machine learning for climate modeling, putting provenance behind predictions.
A PNNL team works to improve AI and machine learning tools so that grid operators can feel confident using them.
Exascale computing, combined with redesigned computational chemistry software, could help researchers develop new renewable energy materials and greener chemical processes.
Revving up chemistry Read Post
PNNL team views ‘undervolting’ — turning down the power supplied to processors — as a way to make exascale computing feasible.
A PNNL team builds models of deep-earth water flows that affect the tiny organisms that can make big contributions to climate-changing gases.
A Pacific Northwest National Laboratory researcher is developing approaches to spread the work evenly over scads of processors in a high-performance computer and to keep calculations clicking even as part of the machine has a hiccup.
A Pacific Northwest National Laboratory computation suggests that the water-gas compounds found in ocean permafrost can provide energy and store it, too – and then trap carbon dioxide.
Twice-stuffed permafrost Read Post
A PNNL team enlists new algorithms and powerful computers to quickly analyze which combinations of failures most threaten the power grid.
Getting a grip on the grid Read Post
Pacific Northwest National Laboratory researchers say their algorithms can analyze millions of video frames, pluck out the faces and quantify them to create searchable databases for facial identification.