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 ...

Increasingly, physics graduates take jobs outside academia. Active teaching approaches lead to deeper conceptual understanding and a more varied skill set and are therefore more likely to prepare ...

As a physics major, it feels like I spend the majority of my waking life solving problems. I’ve calculated the amount of water you get from mixing different ratios of steam and ice, the path of ...

Using an advanced Monte Carlo method, Caltech researchers found a way to tame the infinite complexity of Feynman diagrams and solve the long-standing polaron problem, unlocking deeper understanding of ...

Black physicists say efforts to recruit and retain more Black students must concentrate on challenges they face at both Historically Black Colleges and Universities and Primarily White Institutions.

Recently, I watched a fellow particle physicist talk about a calculation he had pushed to a new height of precision. His tool? A 1980s-era computer program called FORM. Particle physicists use some of ...

Using artificial intelligence, physicists have compressed a daunting quantum problem that until now required 100,000 equations into a bite-size task of as few as four equations—all without sacrificing ...

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.

Researchers trained a machine learning tool to capture the physics of electrons moving on a lattice using far fewer equations than would typically be required, all without sacrificing accuracy. Using ...