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 ...

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 ...

Running a single physics simulation can take hours or days, depending on the complexity of the geometry and the equations involved. For engineers iterating through hundreds of design variations, that ...

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 ...

Simulating how atoms and molecules move over time is a central challenge in computational chemistry and materials science. Classical machine learning approaches to molecular dynamics (MD) encode ...

Dyad AI from JuliaHub is bringing an AI-for-Science environment to product development. Users can model and interrogate systems, research formulations, derive governing equations, assemble models, run ...

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 ...

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 ...

San Mateo, California-based startup Luminary Cloud has released three new physics artificial intelligence models aimed at dramatically accelerating the design of collaborative combat aircraft, ...

Potato is the fourth most important food crop on Earth, and knowing exactly how a potato canopy is growing has long depended on a deceptively simple number: the leaf area index, or LAI, the one-sided ...