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 ...

As the 2026 transfer season approaches, community college physics students are ditching traditional study methods for a dynamic blend of interactive PhET simulations and specialized AI problem-solvers ...

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) ...

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

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

Adrian Macneil has a solid understanding of this space. As an executive at the self-driving startup Cruise, he built the ...

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 ...

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 ...

MIT Technology Review's authoritative overview of the 10 technologies, emerging trends, bold ideas, and powerful movements in AI in 2026.