Morning Overview on MSN
Physics-trained AI models speed up engineering simulations and design work
Running a single physics simulation can take hours or days, depending on the complexity of the geometry and the equations ...
At SAE World Congress 2026, Luminary, the Physics AI company, today announced SHIFT-Crash, the first Physics AI model that predicts full-vehicle crash response, including deformation and stress fields ...
David J. Silvester, a mathematics professor at the University of Manchester, has developed a novel machine-learning method to ...
A recent publication from IMDEA Materials Institute and the Technical University of Madrid (UPM) presents a major step ...
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) ...
Demis Hassabis (DeepMind CEO) and other AI leaders sees the next big AI gains—and the path to AGI—will come from targeted ...
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 ...
15don MSN
Ultra‑robust machine‑learning models run stable molecular simulations at extreme temperatures
Researchers at The University of Manchester have created a physics‑informed machine‑learning model that can run molecular simulations for unprecedented lengths of time, even at temperatures as high as ...
Morning Overview on MSN
Physics-trained AI models speed engineering design and simulations
When engineers at Sumitomo Riko needed to speed up the design cycle for automotive rubber and polymer components, they turned ...
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