Accurately tracking atmospheric greenhouse gases requires not only fast predictions but also reliable estimates of ...

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

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 ...

Artificial intelligence has moved from crunching physics data in the background to actively proposing new theories and experiments. The hope is that these systems might finally expose cracks in the ...

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 ...

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 ...

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

Metals are made of randomly oriented crystals at the microscopic-length scale. The alignment of the crystal faces creates an infinite number of configurations and complex patterns, making simulations ...

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

In 1930, a young physicist named Carl D. Anderson was tasked by his mentor with measuring the energies of cosmic rays—particles arriving at high speed from outer space.