1593 papers
Statistical mechanics explores how the chaotic motion of countless tiny particles gives rise to the predictable laws governing heat, pressure, and phase transitions. This field bridges the gap between the microscopic world of atoms and the macroscopic reality we experience daily, offering deep insights into why materials behave the way they do.
On Gist.Science, we process every new preprint in this category as it appears on arXiv to make these complex findings accessible to everyone. For each paper, we provide both a plain-language explanation for the curious reader and a detailed technical summary for specialists, ensuring that groundbreaking research is never lost behind a wall of jargon.
Below are the latest papers in statistical mechanics, freshly curated and summarized to help you understand the cutting edge of this fascinating discipline.
This paper presents a short-time expansion method for one-dimensional Fokker-Planck equations with spatially dependent diffusion coefficients across general stochastic integral discretization parameters, decomposing the propagator into a closed-form singular term and a regular term computable via a Taylor series with coefficients satisfying ordinary differential equations.
This paper introduces a novel method for characterizing flat regions in continuous constraint satisfaction problems by counting wedged and inscribed spheres, revealing at least two distinct topological regimes in the solution space of the spherical perceptron.
This paper demonstrates that even weak nonreciprocal alignment acts as a generic mechanism to destabilize ordered phases and induce large-scale demixing and structure formation in flocking mixtures, regardless of whether the species mutually align or anti-align.
This paper introduces a fully quantum Metropolis-Hastings algorithm for Integer Linear Programming that utilizes reversible quantum circuits to perform coherent random walks over discrete feasible regions without qRAM or classical pre/post-processing, achieving linear resource scaling and demonstrating effective thermalization toward optimal solutions in numerical simulations.
This paper demonstrates that learning Ising model parameters and structure is computationally feasible using only limited-order sufficient statistics, specifically up to order for a model with width , thereby bridging the gap between computationally hard full-statistic requirements and practical observational constraints.
This paper establishes a constructive framework for analyzing Turing pattern formation on Matrix-Weighted Networks by characterizing coherent structures through node-dependent orthonormal transformations, which allows the reduction of matrix-weighted diffusion to scalar-weighted diffusion to derive generalized instability conditions.
This paper establishes a fundamental trade-off between accuracy and energetic cost for a propelled particle maintaining localization against thermal noise and a constant flow, demonstrating that optimal non-feedback control protocols achieve superior precision by utilizing discontinuous switching between passive and active states with time-dependent diffusivity.
This paper proposes a novel mass matrix tuning strategy based on seismic acquisition geometry to enhance the efficiency and accuracy of Hamiltonian Monte Carlo sampling for solving the ill-posed acoustic Full-Waveform Inversion problem under noisy and limited data conditions.
This paper proposes a Bayesian sequential approach for time-lapse Full Waveform Inversion using Hamiltonian Monte Carlo to effectively quantify uncertainties in high-dimensional seismic problems by integrating baseline survey data as prior knowledge, demonstrating accuracy comparable to parallel schemes while managing computational costs.
This paper presents a pedagogical study of extreme value statistics in a continuous-time branching process by mapping it to an "agitated random walk," enabling the derivation of exact analytical results for the maximal population distribution across subcritical, critical, and supercritical phases, which are validated by numerical simulations.