Thermodynamics and statistical physics provide a unified framework linking the macroscopic laws of energy, work, and entropy to the microscopic motions of particles. Thermodynamics, rooted in the laws ...

Statistical physics of spin systems provides a versatile framework for understanding phase transitions and critical phenomena by modelling collections of interacting discrete variables, or “spins”, ...

In a recent paper, SFI Professor David Wolpert, SFI Fractal Faculty member Carlo Rovelli, and physicist Jordan Scharnhorst ...

Richard Easther and Frank Wang argue that a “Newton first” approach to undergraduate physics teaching can give students a better insight than focusing solely on “modern physics” The whole story Topics ...

A research team led by Prof. PAN Ding, Associate Professor from the Departments of Physics and Chemistry, and Dr. LI Shuo-Hui, Research Assistant Professor from the Department of Physics at the Hong ...

John J. Hopfield and Geoffrey E. Hinton received the Nobel Prize in physics on Oct. 8, 2024, for their research on machine learning algorithms and neural networks that help computers learn. Their work ...

Statistical physicists attempted to explain the brain’s functionality using Excitation-Inhibition balance similarly to magnetic models, yet the theory was experimentally disproven and now, a decade ...

Spin glasses might turn out to be the most useful useless things ever discovered. These materials — which are typically made of metal, not glass — exhibit puzzling behaviors that captivated a small ...

John J. Hopfield and Geoffrey E. Hinton received the Nobel Prize in physics on Oct. 8, 2024, for their research on machine learning algorithms and neural networks that help computers learn. Their work ...

John Hopfield and Geoffrey Hinton were awarded the 2024 Nobel Prize in physics on Tuesday for their contributions to machine learning. Their research, which draws from statistical physics, helped ...

Researchers from The University of New Mexico and Los Alamos National Laboratory have developed a novel computational framework that addresses a longstanding challenge in statistical physics.