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

Erratum and original of Port-Hamiltonian structure of interacting particle systems and its mean-field limit

This paper presents a minimal port-Hamiltonian formulation for interacting particle systems to analyze their stability and mean-field limits, while simultaneously issuing an erratum that corrects a previous claim regarding trajectory compactness by providing a counterexample for repulsive interactions and a revised proof for Hamiltonian gradient convergence.

Jannik Daun, Daniel Jannik Happ, Birgit Jacob, Claudia Totzeck2026-03-10
⚛️ quantum physics

Sequential Quantum Measurements and the Instrumental Group Algebra

This paper introduces the instrumental group algebra (IGA) as a Banach algebra framework for sequential quantum measurements, demonstrating that the time-dependent Kraus-operator density (KOD) evolves via a classical Kolmogorov equation and that combining instruments corresponds to convolution within the IGA, thereby providing a unified mathematical structure for observables that cannot be measured by orthogonal projections.

Christopher S. Jackson2026-03-10
🔢 mathematics

Axial Symmetric Navier Stokes Equations and the Beltrami /anti Beltrami spectrum in view of Physics Informed Neural Networks

This paper establishes the theoretical framework for solving axial symmetric Navier-Stokes equations in a cylindrical topology by constructing a complete basis of harmonic 1-forms comprising Beltrami, anti-Beltrami, and closed components, thereby reducing the problem to a hierarchy of quadratic relations suitable for future optimization via Physics-Informed Neural Networks.

Pietro Fré2026-03-10
🔢 mathematics

Thermodynamics a la Souriau on Kähler Non Compact Symmetric Spaces for Cartan Neural Networks

This paper clarifies the abstract geometrical formulation of thermodynamics on non-compact symmetric spaces used in Cartan Neural Networks by proving that only Kähler spaces support Gibbs distributions, explicitly characterizing their generalized temperature spaces via adjoint orbits, and demonstrating the equivalence between various information and thermodynamical geometries while establishing the covariance of these distributions under the full symmetry group.

Pietro G. Fré, Alexander S. Sorin, Mario Trigiante2026-03-10