AI-powered physics simulations are speeding up design cycles, cutting costs, and enabling more creative engineering solutions. By combining traditional multiphysics modeling with machine learning, ...

Simulating how atoms and molecules move over time is a central challenge in computational chemistry and materials science.

AI excels at correlations but lacks physical intuition, creating gaps in real-world reasoning and reliability.

IBM and Dallara are collaborating on the development of new physics-based AI foundation models. One early model was trained ...

Artificial intelligence is revolutionizing physics by making complex concepts more intuitive, interactive, and personalized. From physics-informed neural networks to AI-powered simulations, these ...

Methodology of the CondensNet model. CondensNet is a physically constrained DL parametrisation coupled with a climate dynamics engine to support hybrid modelling. The network architecture mainly has ...

Researchers present a comprehensive review of frontier AI applications in computational structural analysis from 2020 to 2025, focusing on graph neural networks (GNNs), sequence-to-sequence (Seq2Seq) ...

Design engineering is running headfirst into a materials bottleneck. Industries such as automotive, aerospace, electronics, and semiconductors now depend on increasingly complex materials. Yet ...

The original version of this story appeared in Quanta Magazine. When she was 10 years old, Rose Yu got a birthday present that would change her life—and, potentially, the way we study physics. Her ...

During surgery to correct an abnormal heartbeat, doctors rely on a mix of imaging and inference. Still, many critical details remain hidden. At RIT, artificial intelligence (AI) researchers want to ...