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.

Balanced tensor categories of representations of fixed-points conformal nets

This paper establishes an equivalence of balanced W\mathrm{W}^*-tensor categories between the GG-equivariantization of the category of GG-twisted representations of a conformal net A\mathcal{A} and the category of representations of its fixed-point net AG\mathcal{A}^G, thereby extending a known rational result to the non-rational case while preserving the balanced structure.

Adrià Marín-Salvador2026-06-05🔢 math-ph

AA-Generalized Hessian pre-Lie algebras and AA-Generalized Yang--Baxter Equations

This paper introduces the AA-generalized Yang--Baxter equation and its symmetric solutions via AA-generalized Hessian pre-Lie algebras, establishing a correspondence between factorizable solutions and generalized quadratic Rota--Baxter pre-Lie algebras while providing a structural classification of these algebras through central and double extensions.

Yining Sun, Zeyu Hao, Ziyi Zhang, Liangyun Chen2026-06-04🔢 math-ph

A Systematic Benchmark of Physics-Informed Neural Network Architectures for the Stiff Poisson-Nernst-Planck System: Adaptive LossWeighting and Multi-Scale Resolution

This paper presents a systematic, data-free benchmark of eleven Physics-Informed Neural Network architectures for the stiff Poisson-Nernst-Planck system, demonstrating that the Balanced Residual Decay Rate (BRDR) strategy offers an optimal balance between accuracy and computational efficiency compared to other methods, while providing an open-source implementation for future research.

David Pankaczy, Conrard Giresse Tetsassi Feugmo2026-06-04🔬 physics.app-ph