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 presents a generalized linear response theory for mixed jump-diffusion models that derives comprehensive response formulas and decomposes system behavior into Kolmogorov eigenmodes, successfully demonstrating its predictive power in diagnosing ENSO variability and projecting climate change in energy balance models.
This paper addresses the ambiguity in defining X gates for qudits by identifying three inequivalent formulations of the bit-flip channel, demonstrating that common cyclic models are special cases of these generalizations, and showing how these distinct channels uniquely impact the entanglement of qubit-qutrit and 2-qutrit Werner states.
This paper proposes a rigorous quantum Hamiltonian formalism for renormalization group flows in topologically ordered systems by leveraging the algebraic relationship between homomorphisms, quotient rings, and ideals to characterize generalized symmetries, determine anyon condensation rules, and constrain gapped phase classifications.
This paper derives a thermodynamically consistent free boundary model for two-phase flows in an evolving domain that incorporates bulk-surface interactions via convective Cahn--Hilliard equations and generalized Navier slip conditions, unifying previous models through rigorous derivation via Lagrange Multiplier and Energetic Variational approaches.
This paper explores how reference frame transformations can be modeled as mixed states forming a semigroup rather than pure states forming a group, demonstrating that a quantum system in a pure state relative to one frame can appear as a thermal mixed state relative to another, particularly within the context of Galilean transformations and the time-energy uncertainty relation.
This paper rigorously establishes a solution concept for the linear two-dimensional mountain wave problem by deriving a Helmholtz equation with an altitude-dependent potential and constructing the physically correct solution via a transform method that distinguishes between vertically propagating waves and trapped lee waves under a non-classical radiation condition.
This paper demonstrates that PT-symmetry serves as the fundamental mechanism underlying quasi-integrability in deformed integrable models, ensuring the asymptotic conservation of charges through the definite PT-properties of their Lax pairs and anomalous contributions across various physical systems.
This study utilizes a large-scale, patient-specific computational fluid dynamics framework to demonstrate that specific abdominal aortic aneurysm geometric features reliably dictate hemodynamic patterns, suggesting these geometry-driven flow signatures can serve as valuable biomarkers for predicting aneurysm growth and rupture risk.
This paper develops an age-structured hydrodynamic theory to describe the collective behavior and non-equilibrium steady states of large ensembles of anomalously diffusing particles under stochastic renewal resetting, revealing that while independent resetting yields standard densities, protocols introducing global inter-particle correlations result in steady-state distributions with compact supports.
This paper introduces a novel data-driven framework for discovering stochastic differential equations by unifying Weak SINDy's spatial Gaussian test functions with stochastic system identification, thereby eliminating structural regression bias through unbiased noise projection and enabling the joint sparse recovery of drift and diffusion terms with high accuracy across multiple benchmarks.