3366 papers
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.
This paper introduces and studies fused K-operators of arbitrary spin within the framework of the alternating central extension of the -Onsager algebra, proving that they satisfy the spectral-parameter dependent reflection equation and providing explicit expressions for low-spin cases.
This paper investigates the evolution of torsional rigidity under geometric flows by deriving specific bounds for Heisenberg spaces and homogeneous spheres under Ricci Flow, and establishing comparison inequalities with flat disks for strictly convex free-boundary hypersurfaces under Inverse Mean Curvature Flow.
This paper establishes a connection between the recurrence coefficients of generalised Krawtchouk polynomials and the Painlevé V equation by demonstrating how iterative regularisation of their associated differential system yields polynomial systems and decompositions of birational transformations.
This paper introduces a generalized cluster ladder model that utilizes weak Hopf symmetry to construct a lattice realization of (1+1)D symmetry topological field theory, providing an exact solution via weak Hopf tensor network states for arbitrary fusion category symmetries.
This paper establishes a law of large numbers for the trajectory of quasiparticles in a Toda lattice at thermal equilibrium, demonstrating that they travel with explicit constant velocities through a direct analysis of asymptotic scattering relations and concentration estimates for the system's Lax matrix.
This paper establishes small-time asymptotics for the Anderson Hamiltonian and parabolic Anderson model on bounded planar domains using probabilistic methods, demonstrating that a single observation of the system's eigenvalues or mass can almost surely recover geometric properties of the domain (such as area, boundary length, or fractal dimension) and the noise variance.
This paper demonstrates that tropicalizing scalar quantum field theory yields an exact, non-linear recursion solution for the quantum effective action that enables a polynomial-time algorithm to sample moduli spaces of metric graphs, thereby proving that perturbative QFT computations lie in the polynomial-time complexity class and successfully evaluating the beta function at 50 loops.
This paper introduces ff-bifbox, an open-source, scalable toolbox that integrates FreeFEM and PETSc to perform numerical branch tracing, stability analysis, and time integration for large, time-dependent nonlinear PDEs on adaptively refined 2D and 3D meshes, validated through novel results on the Brusselator, plate buckling, and compressible Navier-Stokes systems.
This paper introduces a unified framework and modular algorithm that overcomes the mathematical challenges of infinite-dimensional state spaces and unbounded Hamiltonians to learn external potentials, such as Coulomb and periodic interactions, using free-fermion models in continuous space.
This paper rigorously characterizes the tail probability of rogue wave formation in 2D pure gravity deep water waves by proving that they most likely arise through dispersive focusing over optimal timescales, utilizing a novel method that combines normal forms and probabilistic techniques to propagate statistical information without requiring Gaussian approximations.