The integration of deep learning techniques and physics-driven designs is reforming the way we address inverse problems, in which accurate physical properties are extracted from complex observations.

As scientific instruments and the literature generate ever larger volumes of data, machine learning (ML) has become essential for organizing, analyzing and interpreting complex information. This ...

A mathematics professor at The University of Manchester has developed a novel machine-learning method to detect sudden changes in fluid behaviour, improving speed and cost of identifying these ...

A study in the Journal of Cosmology and Astroparticle Physics explores how a machine-learning strategy known as transfer ...

AI has started to emerge as one of the most effective technologies being used in cosmology lately. The power of machine ...

The observational track of Typhoon "Danas" (solid line) along with forecasted paths (dashed lines) depicted on the FY-4B satellite visible light imagery at 08:00 BST on July 6, 2025. The dashed lines ...

Tiny grains of dust floating inside a glowing plasma should, according to decades of theory, push and pull on each other in predictable ways. But when physicists at Emory University turned a ...

Space weather forecasting remains a major challenge in heliophysics, as geomagnetic storms continue to pose significant risks to satellite operations, power ...

Imagine trying to prove that 1+1=2, but when you do the calculations, it turns out that the result is off by 0.1%. That scenario is similar to the riddle that’s facing physicists worldwide as they try ...

I'll admit it, I'm a math guy. And recently, I've tried to express the emerging power of large language models (LLMs) in many aspects of life—including education. And it got me thinking: What if ...