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Researchers used machine learning methods on the ORNL Compute and Data Environment for Science, or CADES, to map vegetation communities in the Kougarok Watershed on the Seward Peninsula of Alaska. The colors denote different types of vegetation, such as w

Modeling—Mapping Arctic vegetation

A team of scientists led by Oak Ridge National Laboratory used machine learning methods to generate a high-resolution map of vegetation growing in the remote reaches of the Alaskan tundra.

Using as much as 50 percent lignin by weight, a new composite material created at ORNL is well suited for use in 3D printing.

New composite advances lignin as a renewable 3D printing material

Scientists at the Department of Energy’s Oak Ridge National Laboratory have created a recipe for a renewable 3D printing feedstock that could spur a profitable new use for an intractable biorefinery byproduct: lignin.

ORNL’s Steven Young (left) and Travis Johnston used Titan to prove the design and training of deep learning networks could be greatly accelerated with a capable computing system.

ORNL researchers use Titan to accelerate design, training of deep learning networks

A team of researchers from the Department of Energy’s Oak Ridge National Laboratory has married artificial intelligence and high-performance computing to achieve a peak speed of 20 petaflops in the generation and training of deep learning networks on the