Abstract

A team of scientists from  School of Physics and Astronomy at Queen Mary University of London has developed a novel artificial intelligence method that could revolutionize our understanding of the universe's most mysterious shapes. Using advanced machine learning, researchers can now explore complex geometric spaces, like the fabric of spacetime itself, without relying on traditional symmetry assumptions.

This new algorithm, called AInstein, tackles one of the most complex puzzles in physics and mathematics: finding the precise shape of space under Einstein field equations. Remarkably, it can do so on spaces as intricate as higher-dimensional spheres, opening new avenues for discovery and shedding light on our understanding of the universe.

Peking University, October 22, 2025: A research team led by Prof. Tian Hui  and his Ph.D. student Zhang Jiale  from the School of Earth and Space Sciences, Peking University, has made significant progress in studying stellar magnetic activity. Using observations taken from the Five-hundred-meter Aperture Spherical Radio Telescope (FAST), known as the “China Sky Eye”, the team uncovered a new type of millisecond-scale radio bursts from an M-type star AD Leo. By combining these observations with surface magnetic field measurements, they confirmed that the radio signals originate from small-scale magnetic structures in the starspot regions. This work fills a long-standing gap in our understanding of small-scale magnetic fields on stars beyond the solar system and provides new insights into the mechanisms behind their coronal eruptions and space weather phenomena. The result was recently published in Science Advances.

A new study led by UNLV scientists sheds light on how planets, including Earth, formed in our galaxy – and why the life and death of nearby stars are an important piece of the puzzle.