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.

Elastodynamics from a variational standpoint: integral equalities and inequalities

This paper extends Emmy Noether's variational approach to singular extremals in nonlinear elastodynamics, deriving generalized integral relations that transform into inequalities for thermodynamically admissible solutions and revealing that kinetic energy can be entirely eliminated from the expression for dynamically stored elastic energy even in the presence of shocks.

Yury Grabovsky, Lev Truskinovsky2026-06-09🔢 math-ph

The macroscopic Kaehler metric of Geometric Thermodynamics versus the microscopic one on the Event Manifold: Exact Partition Functions on CV manifolds. Extended Souriau temperatures and spontaneous magnetizations

This paper establishes a unified framework linking macroscopic Geometric Thermodynamics and microscopic Information Geometry by introducing a Kähler metric on thermodynamic manifolds and deriving exact partition functions for Calabi-Vesentini event manifolds, which leads to a generalized Souriau thermodynamics featuring spontaneous symmetry breaking analogous to magnetization and provides exact Gibbs distributions for Cartan Neural Networks.

Pietro Fré, Alexander S. Sorin, Mario Trigiante2026-06-09⚛️ hep-th

Constraint residuals, graph posteriors, and determinant-corrected full-space targets in Bayesian inverse problems

This paper demonstrates that in finite-dimensional Bayesian inverse problems with equality constraints, sampling via penalized residuals in the full parameter-state space yields a posterior distinct from the reduced-space posterior due to a missing Jacobian determinant factor, and it derives specific determinant corrections required to ensure that zero-noise residual limits correctly recover the graph-lifted reduced posterior.

Jonathon Cottom, Emilia Olsson2026-06-09🔢 math-ph