The field of particle physics is approaching a critical horizon defined by challenges including unprecedented data volumes and detector complexity. Upcoming ...

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 ...

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 ...

Machine learning models are being used more and more widely. However, they need a lot of training data to deliver good results. In industrial applications, this wealth of data is often not available ...

Keeping high-power particle accelerators at peak performance requires advanced and precise control systems. For example, the primary research machine at the U.S. Department of Energy's Thomas ...

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

A particle collision reconstructed using the new CMS machine-learning-based particle-flow (MLPF) algorithm. The HFEM and HFHAD signals come from the ...