Accurately tracking atmospheric greenhouse gases requires not only fast predictions but also reliable estimates of uncertainty. Researchers have developed a lightweight machine learning framework that ...

By applying new methods of machine learning to quantum chemistry research, Heidelberg University scientists have made significant strides in computational chemistry. They have achieved a major ...

Researchers employ machine learning to more accurately model the boundary layer wind field of tropical cyclones. Conventional approaches to storm forecasting involve large numerical simulations run on ...

Parisa Khodabakhshi is an assistant professor of mechanical engineering and mechanics in Lehigh University’s P.C. Rossin College of Engineering and Applied Science. Prior to joining the Lehigh faculty ...

The CMS Collaboration has shown, for the first time, that machine learning can be used to fully reconstruct particle ...

Understanding and predicting complex physical systems remain significant challenges in scientific research and engineering. Machine learning models, while powerful, often fail to follow the ...

Iambic Therapeutics, a San Diego–based start-up that harnesses physics and artificial intelligence for drug discovery, is ...

Lithium-ion batteries have become the quiet workhorses of the energy transition, but the way they are designed and tested has long been slow, expensive, and heavily empirical. Machine learning is now ...