The next-generation ShAPE machine has arrived at PNNL, where it will help prove the mettle of the ShAPE extrusion technique. ShAPE 2 is designed to allow researchers to produce larger, more complex extrusions.
The use of disciplines in pure mathematics can increase the reliability and explainability of machine learning models that “transcend human intuition,” according to PNNL scientists.
To overcome high-performance computing bottlenecks, a research team at PNNL proposed using graph theory, a mathematical field that explores relationships and connections between a number, or cluster, of points in a space.
Physicist Emily Mace will share her science journey and an interactive presentation about her current research with middle school and high school students from across the country at the National Science Bowl.
Scientists are pioneering approaches in the branch of artificial intelligence known as machine learning to design and train computer software programs that guide the development of new manufacturing processes.
Two PNNL interns are behind recent innovation in real-time testing and continuous monitoring for pH and the concentration of chemicals of interest in chemical solutions; outcomes have applicability not only to nuclear, but to industries.
Developed at PNNL, Shear Assisted Processing and Extrusion, or ShAPE™, uses significantly less energy and can deliver components like wire, tubes and bars 10 times faster than conventional extrusion, with no sacrifice in quality.
A Q&A with Lauren Charles, veterinarian and PNNL data scientist, on zoonotic diseases and the role biosurveillance plays in mitigating the growing threat to global health.