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.

🔢 mathematics

Global regularity for the Navier-Stokes equations with application to global solvability for the Euler equations

This paper establishes the global regularity of Leray-Hopf weak solutions to the dd-dimensional Navier-Stokes equations for initial data in HsH^s with s1+d/2s \geq -1 + d/2 by constructing a novel supercritical space and deriving viscosity-independent energy estimates through a re-scaling argument, thereby implying global solvability for the Euler equations.

Myong-Hwan Ri2026-01-23
🔢 mathematics

On photonic band gaps in two-dimensional photonic crystal fibres. Analysis in the vicinity of the low-dielectric light line

This paper mathematically analyzes and confirms the existence of photonic band gaps near the low-dielectric light line in two-dimensional photonic crystal fibres, demonstrating their presence in both one-dimensional and ARROW fibre structures through asymptotic analysis without relying on specific dielectric contrast ratios or wave propagation constraints.

Shane Cooper, Ilia Kamotski2026-01-22
🔢 mathematics

Nonlinear projection-based model order reduction with machine learning regression for closure error modeling in the latent space

This paper presents a novel nonlinear projection-based model order reduction framework that utilizes Gaussian process regression and radial basis function interpolation to model closure errors in the latent space, offering improved efficiency, interpretability, and data efficiency compared to deep neural network approaches in complex fluid dynamics applications.

S. Ares de Parga, Radek Tezaur, Carlos G. Hernández, Charbel Farhat2026-01-22