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.

A line of engineering research seeks to develop computers that can tackle a class of challenges called combinatorial optimization problems. These are common in real-world applications such as ...

A machine-learning AI can solve physics problems by simplifying them to be more symmetric. “There are many, many cases in the history of science where people thought things were more complicated than ...

The following is an extract from our Lost in Space-Time newsletter. Each month, we hand over the keyboard to a physicist or two to tell you about fascinating ideas from their corner of the universe.

The Yang–Mills Millennium Prize problem is one of the great challenges of mathematical physics. In the quarter century since it was set, what progress has been made? This Review outlines the problem ...

While atmospheric turbulence is a familiar culprit of rough flights, the chaotic movement of turbulent flows remains an unsolved problem in physics. To gain insight into the system, a team of ...