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 ...

Physics AI engineering simulation tools reached production at General Motors this week, cutting a two-week aerodynamics cycle ...

Researchers at the University of California San Diego and the Allen Institute for AI have built a climate emulator that ...

A study in the Journal of Cosmology and Astroparticle Physics explores how a machine-learning strategy known as transfer ...

AI has started to emerge as one of the most effective technologies being used in cosmology lately. The power of machine ...

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 ...

Space weather forecasting remains a major challenge in heliophysics, as geomagnetic storms continue to pose significant risks to satellite operations, power ...

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 ...

This study introduces a novel method for enhancing Boiling Water Reactor (BWR) operation simulations by integrating machine learning (ML) models with conventional simulation techniques. The ML model ...

image of Winners of the 2024 Nobel Prize in Physics, John J. Hopfield (left) and Geoffrey E. Hinton. Winners of the 2024 Nobel Prize in Physics, John J. Hopfield (left) and Geoffrey E. Hinton. Credit: ...

In developing drugs using a platform that joins physics with machine learning, Schrödinger sees more than a passing resemblance to the studio whose Toy Story and other computer-generated movies ...