In food drying applications, machine learning has demonstrated strong capability in predicting drying rates, moisture ...

The process of testing new solar cell technologies has traditionally been slow and costly, requiring multiple steps. Led by a fifth-year PhD student, a Johns Hopkins team has developed a machine ...

CERES program updates include operational satellite instruments, algorithm advancements, machine learning applications, and ongoing missions measuring Earth’s energy budget and climate system changes.

What if artificial intelligence could turn centuries of scientific literature—and just a few lab experiments—into a smarter, ...

Researchers have developed a framework that uses machine learning to accelerate the search for new proton-conducting materials, that could potentially improve the efficiency of hydrogen fuel cells.

The software tool developed by Stony Brook University uses self-supervised learning to detect long-term solar equipment damage weeks or years before manual inspections find it.

Frederick University and Electi Consulting Ltd have successfully completed the technical implementation of the DYNAMO research project, a decentralised energy management platform designed to allow ...

An astonishing 82 percent decrease in the cost of solar photovoltaic (PV) energy since 2010 has given the world a fighting chance to build a zero-emissions energy system which might be less costly ...

Three decades ago, Yann LeCun, while at Bell Labs, formalized an approach to machine learning called convolutional neural networks that would prove to be profoundly productive in solving tasks such as ...