Machine learning is becoming an essential part of a physicist’s toolkit. How should new students learn to use it? When Radha Mastandrea started her undergraduate physics program at MIT in 2015, she ...

A study in the Journal of Cosmology and Astroparticle Physics explores how a machine-learning strategy known as transfer learning could dramatically reduce the computational cost of searching for new ...

The numerical simulation of physical models is prevalent in science and engineering. These models mathematically represent a physical system, typically by partial differential equations (PDEs) or a ...

Two pioneers of artificial intelligence—John Hopfield and Geoffrey Hinton—won the Nobel Prize in physics Tuesday for helping create the building blocks of machine learning that is revolutionizing the ...

The 2024 Nobel Prize in physics has been awarded to John Hopfield and Geoffrey Hinton for their fundamental discoveries in machine learning, which paved the way for how artificial intelligence is used ...

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 ...

The observational track of Typhoon "Danas" (solid line) along with forecasted paths (dashed lines) depicted on the FY-4B satellite visible light imagery at 08:00 BST on July 6, 2025. The dashed lines ...

Two scientists have been awarded the Nobel Prize in Physics "for foundational discoveries and inventions that enable machine learning with artificial neural networks." John Hopfield, an emeritus ...

As scientific instruments and the literature generate ever larger volumes of data, machine learning (ML) has become essential for organizing, analyzing and interpreting complex information. This ...

Hurricanes, or tropical cyclones, can be devastating natural disasters, leveling entire cities and claiming hundreds or thousands of lives. A key aspect of their destructive potential is their ...