Mathematical physics sits at the fascinating intersection where abstract equations meet the fundamental laws of our universe. This field uses rigorous mathematical tools to model everything from the behavior of subatomic particles to the curvature of spacetime, turning complex theories into testable predictions. It is the language through which physicists describe reality, bridging the gap between pure mathematics and physical observation.

On Gist.Science, we process every new preprint published in this category on arXiv to make these dense studies accessible to everyone. Whether you are a specialist or a curious reader, you will find both plain-language overviews and detailed technical summaries for each paper. Below are the latest mathematical physics papers from arXiv, curated to help you explore the cutting edge of theoretical science.

🌀 nonlinear sciences

Non-stationary difference equation and affine Laumon space III : Generalization to gl^N\widehat{\mathfrak{gl}}_N

This paper introduces a gl^N\widehat{\mathfrak{gl}}_N generalization of a non-stationary difference equation linked to the affine Laumon space, conjecturing that its solutions correspond to affine Laumon partition functions and verifying this connection by demonstrating that the equation reduces to the Fuji-Suzuki-Tsuda system in the four-dimensional limit.

Hidetoshi Awata, Koji Hasegawa, Hiroaki Kanno, Ryo Ohkawa, Shamil Shakirov, Jun'ichi Shiraishi, Yasuhiko Yamada2026-06-16
🔢 mathematics

Universal geometric framework for black hole phase transitions: from multivaluedness to classification

This paper establishes a universal geometric framework linking the synchronized multivaluedness observed in black hole phase transitions to a three-sheeted covering structure arising from two non-degenerate critical points in the temperature function, thereby providing a rigorous classification scheme and a unified perspective on black hole thermodynamics and dynamics.

Shi-Hao Zhang, Zi-Yuan Li, Jing-Fei Zhang, Xin Zhang2026-06-16
🔢 mathematics

Structure-Informed Neural Operators for Long-Time Prediction of Parametric Hamiltonian PDEs

This paper proposes the Energy-Projection Fourier Neural Operator (EP-FNO), a structure-informed architecture that integrates invariant projection with residual FNO updates to significantly enhance long-time stability and accuracy in predicting parametric Hamiltonian PDEs by preserving conserved quantities and reducing phase errors.

Victory C. Obieke, Christopher Chukwuemeka, Emmanuel E. Oguadimma2026-06-16