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 ...

John Hopfield, one of this year’s winners of the Nobel Prize in Physics, is a true polymath. His career started with probing the physics of solid states during the field’s heyday in the 1950s before ...

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.