The U.S. Department of Energy now has two major supercomputing systems aimed at accelerating fusion energy research through artificial intelligence. Argonne National Laboratory’s Aurora exascale ...

Scientists found that transfer learning can make the search for new physics in the universe much faster, slashing the need for expensive simulations. Yet the approach can backfire when AI relies too ...

On the same day IEEE Spectrum reported that General Motors had compressed two weeks of aerodynamics analysis into a matter of minutes using AI trained on simulation data, the broader field that made ...

When engineers at Sumitomo Riko needed to speed up the design cycle for automotive rubber and polymer components, they turned to AI models trained not just on data but on the fundamental equations of ...

Researchers have developed a physics-informed neural network with an attention mechanism that predicts electric vehicle range ...

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 ...

A recurring challenge in science and engineering is the model–reality gap, where trusted legacy simulators lose fidelity due to unresolved physics or structural incompleteness. This challenge has ...

Researchers at the University of Bayreuth have developed a method using artificial intelligence that can significantly speed up the calculation of liquid properties. The AI approach predicts the ...