A collaboration between Harvard University with scientists at QuEra Computing, MIT, University of Innsbruck and other institutions has demonstrated a breakthrough application of neutral-atom quantum ...

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 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 ...

Neural network (NN) has been tentatively combined into multi-objective genetic algorithms (MOGAs) to solve the optimization problems in physics. However, the computationally complex physical ...

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 ...

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 ...

The currents of the oceans, the roiling surface of the sun and the clouds of smoke billowing off a forest fire—all are governed by the same laws of physics, and give rise to a complex phenomenon known ...

A new AI framework called THOR is transforming how scientists calculate the behavior of atoms inside materials. Instead of relying on slow simulations that take weeks of supercomputer time, the system ...

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.