Within the RSI platform, every materials research project is carried by four tightly integrated modules—Project, Session, Experiment and Resource Center—and driven by three layers of deep iterative ...

A dual-graph deep learning network that hard-codes petroleum physics alongside learned statistics cuts oil production prediction error to about 20 percent, far outperforming conventional models.

The FIA World Endurance Championship’s broadcast product races into a vibrant new era in Brazil this weekend (12-13 July), introducing a fresh on-screen visual identity that reflects the series’ core ...

JSTExecutive SummaryIn this survey, we covered materials, batteries, robotic tactile sensing, optical/acoustic control, and ...

What if the most effective physics classroom isn’t a classroom at all, but a skate park? This fall, Traverse City Central ...

Together, these cables connect a collection of sophisticated instruments designed to explore spintronics, an emerging field in quantum computing that uses the spin and magnetic properties of electrons ...

Liquid AI released d1-ultra, a text and image decision model that returns calibrated probabilities in 1 forward pass. It ...

The benchmark confirms that some AIs are becoming exceptional tools for research-level math. But open-source models still ...

AI may soon be the default way to solve theoretical problems in economics, high-energy physics, and other fields, MIT ...

Researchers at Friedrich Schiller University Jena and the University of Regensburg have developed a method for determining, ...

Robotics labs are collecting egocentric video and motion-capture data to train AI models, as the sector raises nearly $48 ...

Integrating Elasto-Plastic and Buckling Analysis of Steel Beams with Unity and C++—1. Introduction In recent years, there has ...