In the first study of its kind, researchers at the RIKEN Center for Interdisciplinary Theoretical and Mathematical Sciences (iTHEMS) in Japan have used artificial intelligence to dramatically speed up the processing time when simulating galaxy evolution coupled with supernova explosion. This approach could help us understand the origins of our own galaxy, particularly the elements essential for life in the Milky Way.

Researchers discover high-velocity clouds in the nearby spiral galaxy M83. These clouds are moving at speeds significantly different to the galaxy’s overall speed of rotation. The findings suggest that these clouds likely originated outside the galaxy, offering new insights into how galaxies acquire fresh gas and sustain star formation over billions of years. This hints at how our own galaxy evolved and may evolve in the future.

An unexpectedly strong solar storm rocked our planet on April 23, 2023, sparking auroras as far south as southern Texas in the U.S. and taking the world by surprise. 

Two days earlier, the Sun blasted a coronal mass ejection (CME) — a cloud of energetic particles, magnetic fields, and solar material — toward Earth. But the CME wasn’t especially fast or massive, suggesting the storm would be minor. But it became severe.

Using NASA heliophysics missions, new studies of this storm and others are helping scientists learn why some CMEs have more intense effects — and better predict the impacts of future solar eruptions on our lives.

SAN ANTONIO — July 1, 2025 —Southwest Research Institute (SwRI) and The University of Texas at San Antonio (UTSA) will receive a $500,000 award from NASA’s TechLeap Prize program to flight test novel electrolyzer technology designed to improve the production of propellants and life-support compounds on the Moon, Mars or near-Earth asteroids. The project, known as the Mars Atmospheric Reactor for Synthesis of Consumables (MARS-C), is led by SwRI’s Kevin Supak and Dr. Eugene Hoffman and UTSA’s Dr. Shrihari “Shri” Sankarasubramanian.

Moving mesh adaptation provides optimal resource allocation to computational fluid dynamics for the capture of different key physical features, i.e., high-resolution flow field solutions on low-resolution meshes. Although many moving mesh methods are available, they require artificial experience as well as computation of a posteriori information about the flow field, which poses a significant challenge for practical applications. Para2Mesh uses a double-diffusion framework to accomplish accurate flow field reconstruction through iterative denoising to provide flow field features as supervised information for fast and reliable mesh movement, thus enabling adaptive mesh prediction from design parameters.

Aircraft safety faces a critical challenge: “stall,” where wings lose lift at high angles, risking crashes. Researchers from the Civil Aviation University of China have developed a bio-inspired solution—microscopic herringbone grooves mimicking bird feathers—that delays stalls by 28.57%. This passive, low-cost technology reduces flow separation on wings, outperforming traditional methods while minimizing drag.